The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index; none of the 53 sources its claims cite is checked for support yet.
Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy
Open accessAbstract
Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 μm to 9 μm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
The paper introduces wavelength-multiplexed hook nanoantennas (WMHNAs) that use loss engineering and broadband 6–9 μm plasmonic resonance to enhance mid-infrared molecular fingerprint detection. Combined with PCA/SVM machine learning, the platform identifies 1% methanol, ethanol, isopropanol, and their mixtures in water with 100% accuracy.
Key findings
- Hook nanoantennas tune radiative loss by changing the geometric difference ΔL between arms; optimal sensitivity occurred at ΔL = 0.65 μm for transmission (extracted loss ratio f = 0.644) and ΔL = 1.25 μm for reflection (f = 1.834), close to theoretical optima of f = 0.5 and f = 2, respectively.
- The 16-element WMHNA provides continuous ultra-broadband operation from 6 μm to 9 μm, matching mid-infrared fingerprint absorption; it captured 15 absorption peaks of methanol, ethanol, isopropanol, and water.
- WMHNA enhanced water scissoring absorption at 6.0 μm by 34.36 times and stretching absorption at 2.95 μm by 4.17 times; compared with direct thin-film measurement, WMHNA and HNA showed significant enhancement of absorption spectra by 3 orders of magnitude.
- PCA extracted multi-dimensional features from refractometric, spectroscopic, and antenna loading effects; SVM classification of 1% alcohol solutions and mixtures achieved 100% accuracy on both training and test sets.
Methods
- Design and simulation: temporal coupled-mode theory (TCMT) modeled plasmon–molecule coupling; finite-difference time-domain (FDTD, Lumerical) simulated far-field spectra and near-field distributions of hook nanoantennas.
- Fabrication and measurement: gold HNAs were fabricated on CaF₂ by electron-beam lithography and lift-off; FTIR microscopy with an MCT detector measured reflection/transmission spectra, and a PDMS microfluidic chamber enabled liquid sensing.
- Machine learning: PCA reduced WMHNA spectra to principal components; SVM with an RBF kernel (C = 1.0, gamma = 0.1) classified analytes. The dataset contained 50 spectra per molecule, split into 40 training and 10 testing spectra.
Limitations
- The paper does not explicitly state limitations.
- Evident from the text: the molecular recognition demonstration is a proof-of-concept limited to three chemically similar alcohols at 1% concentration in water and their mixtures, with a small dataset of 50 spectra per molecule.
- Evident from the text: the WMHNA design shown covers 6–9 μm, while the paper notes that covering all IR fingerprint ranges would require further engineering of antenna length for specific molecular targets.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 53 cited sources checked · 0 supported · 0 flagged
- Findingown data
The broadband resonance peak of wavelength-multiplexed hook nanoantennas is well-matched with the fingerprint absorption of alcoholic liquids methanol, ethanol, and isopropanol.
“The broadband resonance peak of WMHNA is well-matched with the fingerprint absorption of alcoholic liquids of methanol, ethanol, and IPA as shown in Fig. 1c.”
Quote in the paperSubstantial claim - Findingreasoning
Tailoring the radiative-to-absorptive loss ratio of hook nanoantennas improves the sensitivity of molecular vibration detection under the optimal condition supported by temporal coupled-mode theory.
“By tailoring the radiative to absorptive loss ratio, the sensitivity of molecular vibration detected by HNAs can be improved dramatically under the optimal condition supported by temporal coupled-mode theory (TCMT).”
Quote in the paperSubstantial claim - Findingown data
Increasing the refractive index of an analyte causes a redshift of the resonance wavelength of nanoantennas.
“As shown in Fig. 1d-i, the refractometric effect, caused by the refractive index of analytes, brings a redshift of resonance wavelength as n increases.”
Quote in the paperSubstantial claim - Findingown data
The far-field reflection spectrum of wavelength-multiplexed hook nanoantennas covers a broad bandwidth from 6 to 9 μm in the IR fingerprint region.
“The far-field reflection spectrum is shown in Fig. 1b, covering a broad bandwidth from 6 to 9 μm in the IR fingerprint region.”
Quote in the paperSubstantial claim - Findingreasoning
Wavelength-multiplexed hook nanoantennas perform ultra-broadband working wavelengths ranging from 6 μm to 9 μm, matching the mid-infrared fingerprint region.
“Leveraging the gradient change of HNA dimensions, the WMHNA performs ultra-broadband working wavelengths ranging from 6 μm to 9 μm, matching with the MIR fingerprint region.”
Quote in the paperSubstantial claim
- Findingown data
The antenna loading effect is caused by the wavelength mismatch between antenna resonance and molecular vibration.
“The antenna loading effect is caused by the wavelength mismatch between antenna resonance and molecular vibration, as shown in Fig. 1d-iii.”
Quote in the paperSubstantial claim - Findingown data
A substantial intensity change caused by carbonyl stretching is observed at ~5.8 μm in transmission and reflection mode.
“A substantial intensity change caused by the carbonyl stretching is observed at ~5.8 μm in transmission (Fig. 3a) and reflection (Fig. 3b) mode.”
Quote in the paperSubstantial claim - Findingreasoning
The enhanced vibration signal is observed as a Fano-like line shape, a noticeable dip in hook nanoantenna resonance, when the two resonance modes are well-matched (ω0 = ωm).
“From Eqs. 1, 2, the enhanced vibration signal can be observed as a Fano-like line shape, a noticeable dip in HNA resonance when two resonance modes are well-matched (ω0 = ωm).”
Quote in the paperSubstantial claim - Findingreasoning
The radiative loss can be continuously tuned by controlling the folding degree of hook nanoantennas, denoted as ΔL.
“With the design of hook nanoantenna, the radiative loss can be continuously tuned by controlling the folding degree of HNA, which is denoted as ΔL.”
Quote in the paperSubstantial claim - Findingown data
There is a slight increment of intensity (~2 times) from half to full fold of hook nanoantennas due to superposition of the electric field near the two poles of the nanoantennas.
“There is a slight increment of the intensity (~2 times) from half to full fold of HNAs due to the superposition of the electric field near the two poles of the HNAs.”
Quote in the paperNot a substantial claim - Findingown data
Identification results of three major types of alcohols mixed in water at 1% concentration show an obvious decision boundary for each type of target in the principal component domain.
“The identification results of three major types of alcohols mixed in water with 1% concentration are shown in Fig. 1e. Each point in the PC domain refers to one spectrum data from WMHNA, and the color area indicates an obvious decision boundary of each type of target.”
Quote in the paperSubstantial claim - Findingown data
Decreasing ΔL decreases radiative loss due to the increased length of overlapping inverse current in the short arm of hook nanoantennas.
“By decreasing the ΔL, the radiative loss decreases due to the increased length of overlapping inverse current in the short arm of HNA, as shown in Fig. 2c.”
Quote in the paperSubstantial claim - Findingown data
With feature extraction by principal component analysis, sensing signals with different degrees of freedom are identified as different principal components, indicating influence from refractometric, spectroscopic, and antenna loading effects.
“With the feature extraction by PCA, the sensing signal with different degree-of-freedom are identified as different principal components (PCs), indicating the influence from RE, SE, and ALE.”
Quote in the paperSubstantial claim - Findingreasoning
It is still difficult to distinguish clearly with classic data processing methods from the enhanced spectrum of HNA by solely analyzing the fingerprint absorption.
“However, it is still difficult to distinguish clearly with the classic data processing methods from the enhanced spectrum of HNA by solely analyzing the fingerprint absorption.”
Quote in the paperSubstantial claim - Findingreasoning
Due to the low concentration of analytes, the change of reflection at absorption is small and cannot be detected without HNA.
“Due to the low concentration of analytes, the change of reflection at absorption is small and cannot be detected without HNA.”
Quote in the paperSubstantial claim - Findingown data
The highest sensitivity is achieved when the resonance wavelengths of hook nanoantennas and molecules are well-matched.
“In Fig. 4a, b, the highest sensitivity is achieved when the resonance wavelengths of HNA and molecules are well-matched (Fig. 4c).”
Quote in the paperSubstantial claim - Findingown data
The real-time spectrum indicates the analyte change at the WMHNA surface as time goes by.
“As shown in Fig. 5b, the real-time spectrum indicates the analyte change at the WMHNA surface as time goes by.”
Quote in the paperSubstantial claim - Findingown data
The reduction of O-H and H-O-H bond absorption at 2.95 μm and 6.0 μm represents the decrease of water concentration.
“The reduction of O-H and H-O-H bond absorption at 2.95 μm and 6.0 μm represents the decrease of water concentration, while the increasing of C=O, C-H, C-C-C bond absorptance at 5.7 μm, 7.0 and 7.3 μm, 8.3 μm indicate the introduction of acetone molecules into the microfluidic system.”
Quote in the paperSubstantial claim - Findingown data
A two times improvement of sensitivity is observed for the hook nanoantenna with 2.22 μm length compared with the hook nanoantenna with 2.4 μm length.
“A two times improvement of sensitivity is observed by the HNA with 2.22 μm length compared with the HNA with 2.4 μm length.”
Quote in the paperSubstantial claim - Findingown data
The first principal component represents the wavelength shift of HNA resonance by RE.
“The first PC represents the wavelength shift of HNA resonance by RE and the second PC represents the modulation of water absorption peak by ALE.”
Quote in the paperSubstantial claim - Findingown data
After optimization, the arm length ratio is fixed at 1:3 to achieve the highest sensitivity at reflection mode.
“After optimization, the arm length ratio is fixed at 1:3 to achieve the highest sensitivity at reflection mode.”
Quote in the paperSubstantial claim - Findingown data
The second principal component represents the modulation of water absorption peak by ALE.
“The first PC represents the wavelength shift of HNA resonance by RE and the second PC represents the modulation of water absorption peak by ALE.”
Quote in the paperNot a substantial claim - Findingown data
Sensing results of silk and PMMA on the hook nanoantenna supercell show a broadband response from ~5.5 μm to ~8.5 μm.
“Figure 4b shows the sensing results of silk and PMMA on HNA supercell with the broadband response from ~5.5 μm to ~8.5 μm.”
Quote in the paperSubstantial claim - Findingown data
The increasing C=O, C-H, C-C-C bond absorptance at 5.7 μm, 7.0 and 7.3 μm, 8.3 μm indicate the introduction of acetone molecules into the microfluidic system.
“The reduction of O-H and H-O-H bond absorption at 2.95 μm and 6.0 μm represents the decrease of water concentration, while the increasing of C=O, C-H, C-C-C bond absorptance at 5.7 μm, 7.0 and 7.3 μm, 8.3 μm indicate the introduction of acetone molecules into the microfluidic system.”
Quote in the paperSubstantial claim - Findingown data
Multiple fingerprint absorption peaks are captured by the broadband device.
“Multiple fingerprint absorption peaks, labeled in Fig. 4f, are captured by the broadband device.”
Quote in the paperSubstantial claim - Findingown data
The response of WMHNA for water shows an enhancement factor of 4.17 for the stretching absorption at 2.95 μm due to the localized electric field.
“The response of WMHNA for water with the enhancement of stretching absorption at 2.95 μm and scissoring absorption at 6.0 μm is shown in Fig. 5d. The stretching mode is in the non-resonant regime of WMHNA, showing an enhancement factor of 4.17 thanks to the localized electric field.”
Quote in the paperNot a substantial claim - Findingown data
Multiple fingerprint absorption peaks are captured to have rich information of chemical bond changes.
“Multiple fingerprint absorption peaks are captured to have rich information of chemical bond changes.”
Quote in the paperSubstantial claim - Findingown data
The spectrum of the 16-element wavelength-multiplexed hook nanoantenna array shows a wavelength-multiplexed response from ~5 μm to ~7.8 μm by changing the length L.
“The spectrum of the 16-element WMHNA is shown in Fig. 4d, showing the wavelength-multiplexed response from ~5 μm to ~7.8 μm by changing the L.”
Quote in the paperSubstantial claim - Findingnone given
The third principal component represents the fingerprint absorption of three molecules by SE.
“The third PC represents the fingerprint absorption of three molecules by SE.”
Quote in the paperSubstantial claim - Findingown data
The accuracy of the SVM classifier for both the training set and test set is 100%.
“Finally, the accuracy of this classifier for both the training set and test set is 100%.”
Quote in the paperSubstantial claim - Findingown data
With the mixture of diluted alcoholic solutions, the microfluidic-integrated WMHNA captures 15 absorption peaks of methanol, ethanol, isopropanol, and water in 6 to 9 μm wavelengths.
“With the mixture of diluted alcoholic solutions, the microfluidic-integrated WMHNA captures 15 absorption peaks of methanol, ethanol, isopropanol, and water in 6 to 9 μm wavelengths.”
Quote in the paperSubstantial claim - Premisecitation
Molecular enrichment strategies bring additional working requirements for specific molecules, impeding global molecular recognition.
“The molecular enrichment strategies, including functionalized chemical bonding17,18, chemical reaction19,20, physical adsorption21–23, optical trapping24 as well as passive trapping by undercut structure25–28 bring in the additional working requirements (e.g., chemical stimuli, temperature, pressure, pump power, etc.) for specific molecules, impeding the system for global molecular recognition.”
Quote in the paperSubstantial claim0 of 10 sources checked- cites [17] Limaj, O. et al. Infrared Plasmonic Biosensor for Real-Time and Label-Free Monitoring of Lipid Membranes. Nano Lett. 16, 1502–1508 (2016). source
- cites [18] Etezadi, D. et al. Nanoplasmonic mid-infrared biosensor for in vitro protein secondary structure detection. Light Sci. Appl. 6, e17029–e17029 (2017). source
- cites [19] Hasan, D. & Lee, C. Hybrid Metamaterial Absorber Platform for Sensing of CO2 Gas at Mid-IR. Adv. Sci. 5, (2018). source
- cites [20] Chang, Y. et al. All-Dielectric Surface-Enhanced Infrared Absorption-Based Gas Sensor Using Guided Resonance. ACS Appl. Mater. Interfaces 10, 38272–38279 (2018). source
- cites [21] Hu, H. et al. Gas identification with graphene plasmons. Nat. Commun. 10, 1131 (2019). source
- cites [22] Zhou, H. et al. Metal–Organic Framework‐Surface‐Enhanced Infrared Absorption Platform Enables Simultaneous On‐Chip Sensing of Greenhouse Gases. Adv. Sci. 7, 2001173 (2020). source
- cites [23] Nuñez, J., Boersma, A., Grand, J., Mintova, S. & Sciacca, B. Thin Functional Zeolite Layer Supported on Infrared Resonant Nano‐Antennas for the Detection of Benzene Traces. Adv. Funct. Mater. 31, 2101623 (2021). source
- cites [24] Yoo, D. et al. Low-Power Optical Trapping of Nanoparticles and Proteins with Resonant Coaxial Nanoaperture Using 10 nm Gap. Nano Lett. 18, 3637–3642 (2018). source
- cites [25] Miao, X., Yan, L., Wu, Y. & Liu, P. Q. High-sensitivity nanophotonic sensors with passive trapping of analyte molecules in hot spots. Light Sci. Appl. 10, 5 (2021). source
- cites [26] Hwang, I. et al. Plasmon-Enhanced Infrared Spectroscopy Based on Metamaterial Absorbers with Dielectric Nanopedestals. ACS Photonics 5, 3492–3498 (2018). source
- Premisecitation
Multi-resonant plasmonic nanoantenna sensors collect broadband spectrum data to recognize lipids and proteins from separate absorption wavelengths.
“Therefore, to detect more absorption peaks in the MIR region, multi-resonant PNA sensors are proposed to collect broadband spectrum data to recognize lipids and proteins from separate absorption wavelengths39.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [39] Rodrigo, D. et al. Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces. Nat. Commun. 9, (2018). source
- Premisecitation
Plasmonic nanoantennas enhance IR fingerprint absorption by tailored plasmonic resonance.
“artificially structured nanoantenna is demonstrated to enhance the IR fingerprint absorption by tailored plasmonic resonance12,13.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [12] Neubrech, F. et al. Resonant Plasmonic and Vibrational Coupling in a Tailored Nanoantenna for Infrared Detection. Phys. Rev. Lett. 101, 157403 (2008). source
- cites [13] Adato, R. et al. Ultra-sensitive vibrational spectroscopy of protein monolayers with plasmonic nanoantenna arrays. Proc. Natl Acad. Sci. 106, 19227–19232 (2009). source
- Premisecitation
A deep learning method using multi-resonant plasmonic nanoantennas augments dynamics monitoring between four major classes of biomolecule from absorption spectra at three working wavelengths of 3.4 μm, 6.5 μm, and 9 μm.
“Leveraging the multi-resonant PNAs, the deep learning method is proposed to augment the dynamics monitoring between four major classes of biomolecule (lipids, proteins, nucleic acids, and carbohydrates) from the absorption spectra in three working wavelengths of 3.4 μm, 6.5 μm, and 9 μm50.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [50] John‐Herpin, A., Kavungal, D., Mücke, L. & Altug, H. Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules. Adv. Mater. 33, 2006054 (2021). source
- Interpretationreasoning
The concentration of analytes can be read out from the intensity drops in the reflection spectrum.
“Therefore, the concentration of analytes can be read out from the intensity drops from the reflection spectrum.”
Quote in the paperSubstantial claim - Interpretationown data
The light-matter interaction between nanoantennas and molecules can be distinguished with three significant effects: refractometric effect, spectroscopic effect, and antenna loading effect.
“The light-matter interaction between nanoantennas and molecules can be distinguished with three significant effects – the refractometric effect (RE), spectroscopic effect (SE), and antenna loading effect (ALE).”
Quote in the paperSubstantial claim - Premisenone given
In the IR fingerprint region, plasmonic nanoantenna signals of absorption changes and wavelength shifts always come together, hindering the use of complementary information about the analyte's physical and chemical properties for molecule identification.
“Unfortunately, in the IR fingerprint region, the PNA signal of absorption changes and wavelength shifts always comes together, hindering the usage of complementary information about the analyte’s physical and chemical properties for molecule identification.”
Quote in the paperSubstantial claim - Premisereasoning
Molecular recognition capabilities are limited to two specific molecules due to narrow bandwidth at an operating wavelength around 6 μm.
“However, the molecular recognition capabilities are limited to two specific molecules due to the narrow bandwidth at an operating wavelength of around 6 μm.”
Quote in the paperSubstantial claim - Interpretationreasoning
A larger refractive index of analytes brings a longer resonance wavelength of nanoantennas due to an increment of effective optical length proportional to the refractive index of the surrounding environment and the physical length of nanoantennas.
“Larger n of analytes brings a longer resonance wavelength of nanoantennas due to the increment of effective optical length, proportional to the refractive index of surrounding environments and the physical length of nanoantennas.”
Quote in the paperSubstantial claim - Interpretationreasoning
In real sensing applications, the refractive index and absorption coefficient of analytes vary simultaneously when changing concentration, resulting in a complex spectral change combining refractometric, spectroscopic, and antenna loading effects.
“In real sensing applications, the n and k of analytes always vary simultaneously when changing the concentration of any molecules, resulting in a complex change of spectrum with the combination of RE, SE, and ALE.”
Quote in the paperSubstantial claim - Premisecitation
Individual resonances of plasmonic nanoantennas by different order resonance modes fail to cover the whole IR fingerprint wavelength region from 5.5 μm to 10 μm because of gaps between resonance peaks.
“Nevertheless, the individual resonances of PNA by a different order of resonance modes also fail to cover the whole spectrum of IR fingerprint wavelength region from 5.5 μm to 10 μm because of the gaps between two resonance peaks40,41.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [40] Hasan, D., Ho, C. P. & Lee, C. Realization of Fractal-Inspired Thermoresponsive Quasi-3D Plasmonic Metasurfaces with EOT-Like Transmission for Volumetric and Multispectral Detection in the Mid-IR Region. ACS Omega 1, 818–831 (2016). source
- cites [41] Rodrigo, D., Tittl, A., John-Herpin, A., Limaj, O. & Altug, H. Self-Similar Multiresonant Nanoantenna Arrays for Sensing from Near- to Mid-Infrared. ACS Photonics 5, 4903–4911 (2018). source
- Interpretationreasoning
The coupling efficiency μ and the loss ratio f are two degrees of freedom to optimize sensing performance.
“From Eqs. 3 and 4, we can find that μ and f are two degrees of freedom to optimize the sensing performance, highlighted in Fig. 2a.”
Quote in the paperSubstantial claim - Interpretationreasoning
The hook shape in nanoantenna design engineers radiative loss γr to tune radiation from electron oscillation by inducing inverse current from dipole resonance.
“Therefore, the philosophy to use hook shape in nanoantenna design is to engineer γr to tune the radiation from electron oscillation by inducing inverse current from dipole resonance.”
Quote in the paperSubstantial claim - Interpretationreasoning
Compared to the dramatic change of radiative loss, the loss ratio f is a more dominant affecting parameter in different hook nanoantennas.
“However, compared to the dramatic change of radiative loss, the loss ratio (f) is a more dominant affecting parameter in different HNAs.”
Quote in the paperSubstantial claim - Interpretationreasoning
It is not easy to distinguish methanol, ethanol, and isopropanol in a mixture with a narrowband HNA.
“Therefore, it is not easy to distinguish them in a mixture with a narrowband HNA.”
Quote in the paperSubstantial claim - Interpretationreasoning
Thanks to the enhancement of vibration absorption and the reduction of optical path length, the WMHNA platform is suitable for liquid sample analysis using water as a common solvent.
“Thanks to the enhancement of vibration absorption and the reduction of optical path length, our WMHNA platform is suitable for liquid sample analysis using water as a common solvent, paving the way to protentional application in biological sample screening.”
Quote in the paperSubstantial claim - Interpretationown data
By integrating the absorbance spectrum, the dynamic behavior of water and acetone can be monitored by changing different chemical bonds.
“By integrating the absorbance spectrum, the dynamic behavior of water and acetone can be monitored by changing different chemical bonds (Fig. 5e).”
Quote in the paperSubstantial claim - Interpretationnone given
The ultra-confined electromagnetic field near the antenna surface shortens the effective optical path length to subwavelength scales.
“The secret is that the ultra-confined electromagnetic field near the antenna surface shortens the effective optical path length to subwavelength scales.”
Quote in the paperNot a substantial claim - Premisecitation
In PNA-based SEIRAS, the great challenge is the limited bandwidth of nanoantenna resonances compared with the ultra-broadband vibrational absorption fingerprints.
“In PNA-based surface-enhanced IR absorption spectroscopy (SEIRAS), the great challenge is the limited bandwidth of nanoantenna resonances compared with the ultra-broadband vibrational absorption fingerprints.”
Quote in the paperSubstantial claim0 of 7 sources checked- cites [22] Zhou, H. et al. Metal–Organic Framework‐Surface‐Enhanced Infrared Absorption Platform Enables Simultaneous On‐Chip Sensing of Greenhouse Gases. Adv. Sci. 7, 2001173 (2020). source
- cites [39] Rodrigo, D. et al. Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces. Nat. Commun. 9, (2018). source
- cites [41] Rodrigo, D., Tittl, A., John-Herpin, A., Limaj, O. & Altug, H. Self-Similar Multiresonant Nanoantenna Arrays for Sensing from Near- to Mid-Infrared. ACS Photonics 5, 4903–4911 (2018). source
- cites [50] John‐Herpin, A., Kavungal, D., Mücke, L. & Altug, H. Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules. Adv. Mater. 33, 2006054 (2021). source
- cites [54] Hasan, D. et al. Thermoplasmonic Study of a Triple Band Optical Nanoantenna Strongly Coupled to Mid IR Molecular Mode. Sci. Rep. 6, 22227 (2016). source
- cites [55] Aslan, E. et al. Multispectral Cesaro-Type Fractal Plasmonic Nanoantennas. ACS Photonics 3, 2102–2111 (2016). source
- cites [56] Ren, Z., Dang, Z. & Lee, C. Multi-Band Mid-IR Molecules Identification Using Plasmonic Metamaterials Induced by Bright-Dark Coupling. in 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS) vols 2020-Janua 725–728 (IEEE, 2020). source
- Interpretationreasoning
With the full utilization of multiple dimension information, the recognition becomes more efficient by monitoring the complementary physical (refractive index) and chemical properties (absorption fingerprints) of molecules.
“With the full utilization of multiple dimension information, the recognition becomes more efficient by monitoring the complementary physical (refractive index) and chemical properties (absorption fingerprints) of molecules, bringing in another degree of freedom into IR spectroscopy analysis by refractometry and plasmonic properties.”
Quote in the paperSubstantial claim - Interpretationreasoning
With the aid of dimension reduction by PCA, the multi-dimensional information from HNA is easily decoupled and analyzed.
“Furthermore, with the aid of dimension reduction by PCA, the multi-dimensional information from HNA is easily decoupled and analyzed, paving the way to achieve global molecular identification and real-time monitoring by training with deep neural networks (DNN).”
Quote in the paperSubstantial claim - Interpretationown data
Based on the massive spectral data collected by the WMHNA-SEIRAS platform, machine-learning-enabled spectroscopy extracts multi-dimensional features from refractometric effect, spectroscopic effect, and antenna loading effect.
“Furthermore, based on the massive spectral data collected by the WMHNA-SEIRAS platform, we develop machine-learning-enabled spectroscopy to extract the multi-dimensional features from refractometric effect, spectroscopic effect, and antenna loading effect.”
Quote in the paperSubstantial claim - Interpretationreasoning
Thanks to the high sensitivity and broad bandwidth, molecular identification can be achieved from the complementary physical (refractive index) and chemical (absorption fingerprint) properties of molecules.
“Thanks to the high sensitivity and broad bandwidth, molecular identification can be achieved from the complementary physical (refractive index) and chemical (absorption fingerprint) properties of molecules.”
Quote in the paperSubstantial claim - Interpretationreasoning
Compared with single nanoantenna designs with low quality factors, the wavelength of molecular absorption is matched with the specific hook antenna elements in WMHNA, maximizing the sensitivity caused by antenna loading effects.
“On the other hand, compared with the single nanoantenna designs with low quality factors, the wavelength of molecular absorption is matched with the specific hook antenna elements in WMHNA, maximizing the sensitivity caused by antenna loading effects.”
Quote in the paperSubstantial claim - Interpretationcitation
Using the design principle of wavelength multiplexing, the WMHNA can be designed to cover all IR fingerprint ranges by engineering the antenna length for specific molecular monitoring like proteins, sugars, lipids, nucleic acids, and volatile organic compounds (VOCs).
“Using the design principle of wavelength multiplexing, the WMHNA can be designed to cover all IR fingerprint ranges by engineering the antenna length for specific molecular monitoring like proteins48, sugars49, lipids17, nucleic acids57, and volatile organic compounds (VOCs)58.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [48] Semenyshyn, R. et al. In Vitro Monitoring Conformational Changes of Polypeptide Monolayers Using Infrared Plasmonic Nanoantennas. Nano Lett. 19, 1–7 (2019). source
- cites [49] Kühner, L. et al. Vibrational Sensing Using Infrared Nanoantennas: Toward the Noninvasive Quantitation of Physiological Levels of Glucose and Fructose. ACS Sens. 4, 1973–1979 (2019). source
- cites [17] Limaj, O. et al. Infrared Plasmonic Biosensor for Real-Time and Label-Free Monitoring of Lipid Membranes. Nano Lett. 16, 1502–1508 (2016). source
- The quote only partly states this claim. 2
- Probably not a substantial claim. 10
- It may state two separate claims as one. 29
The quote only partly states this claim.
Findingown dataPrincipal component analysis together with support vector machine achieves 100% identification accuracy for molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) at 1% concentration in water using microfluidic integrated wavelength-multiplexed hook nanoantennas.
“With the aid of principal component analysis (PCA) together with supporting vector machine (SVM), the proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy.”
- Also stated in: § Abstract · boosted
“The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs.”
Quote in the paperSubstantial claim- Also stated in: § Abstract · boosted
Probably not a substantial claim. The paper may give no reason why it holds.
Findingnone givenHook nanoantennas perform enhanced sensitivity by reducing radiative loss through folded structures.
“In addition to the bandwidth, the hook nanoantenna also performs an enhanced sensitivity thanks to reducing radiative loss by folded structures.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
FindingreasoningFor two molecules with the same absorption strength, smaller intensity drops are read out when the wavelength mismatch increases, indicating a larger refractive index.
“For two molecules with the same absorption strength, small intensity drops can be read out when the wavelength mismatch increase, indicating a larger refractive index.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataThe highest sensitivity of transmission mode occurs when ΔL equals 0.65 μm, while the highest sensitivity of reflection mode occurs when ΔL equals 1.25 μm.
“From this figure, we observe that the highest sensitivity of transmission mode (ΔT) comes when ∆L equals 0.65 μm, while 1.25 μm for reflection mode (ΔR).”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataAs ΔL decreases, coupling efficiency μ remains almost unchanged because of similar nearfield intensity, but loss ratio f decreases due to reduced radiative loss γr caused by the short electrical length of the antenna and similar absorptive loss γa caused by the same antenna length.
“As ∆L decreases, μ remains almost unchanged because of the similar intensity of near field (Fig. 2f), but f decreases due to the reduced γr caused by the short electrical length of the antenna and the similar γa caused by the same antenna length.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingnone givenThe radiative loss γa is related to the ohmic loss of the plasmonic material and is almost the same among different antenna structures.
“The γa is related to the ohmic loss of plasmonic material (e.g., Au in this work) and is almost the same among different antenna structures.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataThe absorption signal reaches a peak value when ΔL equals 0.6 μm, which indicates the critical coupled point (γa = γr) of the hook nanoantenna resonator.
“However, the absorption signal reaches a peak value when ∆L equals 0.6 μm, which means the critical coupled point (γa = γr) of the HNA resonator.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Findingown dataWith a decrease in ΔL, both transmission and reflection intensities drop, meaning the antenna becomes less radiative (darker).
“With the decrease of ∆L, both T (Fig. 2d-i) and R (Fig. 2d-ii) intensities drop, which means the antenna becomes less radiative (darker).”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataRadiative loss decreases as ΔL decreases, but absorptive loss is almost unchanged, resulting in a small loss ratio f with short ΔL.
“As shown in Fig. 3c, the radiative loss decreases as ∆L decreases, but the absorptive loss is almost unchanged, resulting in a small f with short ∆L.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataThe maximum sensitivity occurs when the loss ratio f reaches 0.5 for transmission mode and 2 for reflection mode.
“The maximum sensitivity occurs when f reaches 0.5 and 2 for transmission and reflection modes, respectively.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataThe enhancement of the electric field intensity of all hook nanoantenna devices is at the order of 10^5, and the maximum enhancement reaches 832891 times.
“The results show that the enhancement of the electric field intensity of all HNA devices is at the order of 105, and the maximum enhancement of the electric field intensity reaches 832891 times.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataBoth wavelength-multiplexed hook nanoantennas and hook nanoantennas perform significant enhancement (3 orders of magnitude) of the absorption spectrum compared with direct measurement of thin-film without nanoantenna.
“Both WMHNA and HNA perform the significant enhancement (3 orders of magnitude) of absorption spectrum with the direct measurement of thin-film without nanoantenna.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Findingnone givenThe apparent dips of the reflection spectrum at 6.0 μm are induced by water scissoring absorption.
“The apparent dips of the reflection spectrum at 6.0 μm are induced by water scissoring absorption.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Findingown dataThe redshift of the hook nanoantenna supercell spectrum is caused by the effect induced by the refractive index of analytes, indicating the refractometric effect of wavelength-multiplexed hook nanoantennas.
“The redshift of the HNA supercell spectrum is caused by the effect induced by the refractive index of analytes indicating the RE of WMHNA.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataWavelength-multiplexed hook nanoantennas better enhance multiple absorption peaks from broad wavelength ranges, while hook nanoantennas only reach the best enhancement at narrow wavelength ranges near resonance wavelengths.
“We further compared the sensing performance with selected HNA elements (P1, P8, and P16) from WMHNA, showing that WMHNA better enhances multiple absorption peaks from broad wavelength ranges, while HNA only reaches the best enhancement at narrow wavelength ranges near resonance wavelengths.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
FindingreasoningThe absorption of water in IR has minimal influence on the nanoantenna spectrum apart from the molecular vibration regime, allowing liquid recognition with water-based solvent.
“Furthermore, the absorption of water in IR has a minimal influence on the nanoantenna spectrum apart from the molecular vibration regime, allowing the liquid recognition with water-based solvent, which is so absorptive in traditional IR spectroscopy that the intensity of light becomes very weak to identify molecules.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataEach curve indicates the real-time spectrum of a mixed solvent of acetone and water, reflecting in-situ concentration information of acetone and water and dynamic change versus time.
“Each curve indicates the real-time spectrum of a mixed solvent of acetone and water, reflecting in-situ concentration information of acetone and water and dynamic change versus time.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataThe fingerprint absorption of small molecule alcohols is captured from 6.5 μm to 9 μm and is extracted from the WMHNA spectrum.
“The fingerprint absorption of small molecule alcohols is captured from 6.5 μm to 9 μm and is extracted from the WMHNA spectrum in Fig. 6b.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataWavelength-multiplexed hook nanoantennas show less influence from the antenna loading effect in the broadband wavelength range, indicating good performance for sensing broadband fingerprint absorption.
“However, WMHNA performs less influence from ALE in the broadband wavelength range, showing good performance for sensing broadband fingerprint absorption.”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataIn the resonant regime, an improvement of 34.36 times of reflection change is observed for the scissoring mode at 6.0 μm.
“In the resonant regime, an improvement of 34.36 times of reflection change is observed for the scissoring mode.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataThe classifier trained with two PCs works well, with the different gases correctly identified and the clusters being well contained in the appropriate regions.
“It can be seen that the classifier trained with two PCs works well, with the different gases correctly identified and the clusters being well contained in the appropriate regions.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Findingown dataIn the 3D PC space, each point represents the spectrum data from WMHNA, and each cluster represents one type of molecule combination.
“In the 3D PC space shown in Fig. 6f, each point represents the spectrum data from WMHNA, and each cluster represents one type of molecule combination.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
FindingcitationCompared with previous literature that demonstrates the identification of two molecules mixture by monitoring two absorption peaks, this work demonstrated simultaneously monitoring of 15 absorption peaks and used to identify three molecules mixture.
“Compared with previous literature that demonstrates the identification of two molecules mixture by monitoring two absorption peaks49, our work demonstrated simultaneously monitoring of 15 absorption peaks and used to identify three molecules mixture.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [49] Kühner, L. et al. Vibrational Sensing Using Infrared Nanoantennas: Toward the Noninvasive Quantitation of Physiological Levels of Glucose and Fructose. ACS Sens. 4, 1973–1979 (2019). source
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
Findingnone givenWith the reverse current induced in HNA, the sensitivity of HNA is improved by engineering the radiative loss to achieve the optimized loss ratio.
“On one hand, with the reverse current induced in HNA, the sensitivity of HNA is improved by engineering the radiative loss to achieve the optimized loss ratio.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
Findingown dataWith the aid of a machine learning algorithm (PCA and SVM), the multi-dimensional information can be classified effectively, resulting in 100% accuracy from random 4:1 data spiting for training and recognition sets.
“Additionally, with the aid of a machine learning algorithm (PCA and SVM), the multi-dimensional information can be classified effectively, resulting in 100% accuracy from random 4:1 data spiting for training and recognition sets.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
Premisenone givenThe sensitivity and bandwidth of plasmonic nanoantennas are limited by the small overlap between molecules and sensing hotspots and by the sharp plasmonic resonance peaks.
“However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks.”
Quote in the paperNot a substantial claimIt may state two separate claims as one.
PremisecitationSqueezing the gap between adjacent plasmonic nanoantennas to the nanometer scale increases electric field intensity but ruins sensing performance by decreasing active area and increasing fabrication cost.
“The other method to improve the sensitivity is to increase the intensity of the electric field by squeezing the gap between adjacent PNA into the nanometer scale29–31. However, the narrow gap ruins the sensing performance by a decrement in the active area and increases fabrication cost.”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [29] Huck, C. et al. Surface-Enhanced Infrared Spectroscopy Using Nanometer-Sized Gaps. ACS Nano 8, 4908–4914 (2014). source
- cites [30] Dong, L. et al. Nanogapped Au Antennas for Ultrasensitive Surface-Enhanced Infrared Absorption Spectroscopy. Nano Lett. 17, 5768–5774 (2017). source
- cites [31] Su, D.-S., Tsai, D. P., Yen, T.-J. & Tanaka, T. Ultrasensitive and Selective Gas Sensor Based on a Channel Plasmonic Structure with an Enormous Hot Spot Region. ACS Sens. 4, 2900–2907 (2019). source
Probably not a substantial claim. The paper’s conclusions may not depend on it.
PremisecitationThe amplification effect caused by strong plasmon-molecule coupling between plasmonic resonance and molecular vibration is well-explained by temporal coupled-mode theory (TCMT).
“This amplification effect caused by the strong plasmon-molecule coupling between plasmonic resonance and molecular vibration was well-explained by temporal coupled-mode theory (TCMT)14,15.”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [14] Adato, R. & Altug, H. In-situ ultra-sensitive infrared absorption spectroscopy of biomolecule interactions in real time with plasmonic nanoantennas. Nat. Commun. 4, 2154 (2013). source
- cites [15] Adato, R., Artar, A., Erramilli, S. & Altug, H. Engineered Absorption Enhancement and Induced Transparency in Coupled Molecular and Plasmonic Resonator Systems. Nano Lett. 13, 2584–2591 (2013). source
It may state two separate claims as one.
PremisecitationWith machine learning, decoupling IR fingerprint absorption and refractive index change has been reported to monitor protein dynamics of secondary structure α-helix and β-sheet and recognition of glucose and fructose.
“With the aid of machine learning (ML), decoupling the IR fingerprint absorption and refractive index change induced by molecules has been reported to monitor protein dynamics of secondary structure α-helix and β-sheet48 and recognition of physiological bio-marker of glucose and fructose49.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [48] Semenyshyn, R. et al. In Vitro Monitoring Conformational Changes of Polypeptide Monolayers Using Infrared Plasmonic Nanoantennas. Nano Lett. 19, 1–7 (2019). source
- cites [49] Kühner, L. et al. Vibrational Sensing Using Infrared Nanoantennas: Toward the Noninvasive Quantitation of Physiological Levels of Glucose and Fructose. ACS Sens. 4, 1973–1979 (2019). source
Probably not a substantial claim.
PremisecitationPlasmonic nanoantennas serve as ultra-sensitive refractometry sensors to capture the refractive index of analytes by wavelength shifts.
“Thanks to the excellent field confinement at resonance wavelength, plasmonic nanoantennas (PNAs) also serve as an ultra-sensitive refractometry sensor to capture the refractive index of analytes by wavelength shifts (e.g., color change in visible light), which carries the information of physical properties of molecules44,45.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [44] Gao, B. et al. Nanoscale Refractive Index Sensors with High Figures of Merit via Optical Slot Antennas. ACS Nano 13, 9131–9138 (2019). source
- cites [45] Zhan, C., Liu, B.-W., Tian, Z.-Q. & Ren, B. Determining the Interfacial Refractive Index via Ultrasensitive Plasmonic Sensors. J. Am. Chem. Soc. 142, 10905–10909 (2020). source
It may state two separate claims as one.
PremisereasoningMolecules of the same class have strong overlap in fingerprint absorption due to similar chemical structure, making it challenging to distinguish them in small concentrations by monitoring a single absorption peak.
“However, since the same classes of molecules always have a strong overlap in fingerprint absorption due to the similar chemical structure, it is challenging to distinguish the same classes of molecules in small concentrations by monitoring single absorption peak of each molecules.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
Interpretationnone givenThe coupling efficiency is related to the nearfield intensity and the overlapping of molecules with the antenna nearfield.
“The coupling efficiency is related to the nearfield intensity and the overlapping of molecules to the antenna nearfield.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
PremisereasoningAll three methods-hotspot release, molecule enrichment, and nanogap squeezing-introduce extra processes or materials, increasing fabrication cost or limiting working conditions.
“However, all of these three methods introduce extra processes or materials to make the plasmonic sensors, which increase the fabrication cost or limit the working condition.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
PremisereasoningThree approaches to optimize nanoantenna sensing by increasing coupling efficiency μ are hotspot release, molecule enrichment, and squeezing adjacent nanoantennas into a nanogap.
“Figure 2b summarizes three approaches to optimize nanoantenna sensing by increasing μ. Hotspots release is proposed to increase accessible sensing areas for analytes, which are usually blocked by the substrate, to further enhance the coupling efficiency25–28.”
- Also stated in: § Design principles of hook nanoantenna · plain
“Therefore, the molecule enrichment method is utilized to accumulate localized molecules in the effective sensing area17–24. Another approach to enhance the nearfield coupling is to increase the electric field intensity by squeezing the adjacent nanoantenna into the nanogap29–31.”
Quote in the paperNot a substantial claim0 of 10 sources checked- cites [17] Limaj, O. et al. Infrared Plasmonic Biosensor for Real-Time and Label-Free Monitoring of Lipid Membranes. Nano Lett. 16, 1502–1508 (2016). source
- cites [18] Etezadi, D. et al. Nanoplasmonic mid-infrared biosensor for in vitro protein secondary structure detection. Light Sci. Appl. 6, e17029–e17029 (2017). source
- cites [19] Hasan, D. & Lee, C. Hybrid Metamaterial Absorber Platform for Sensing of CO2 Gas at Mid-IR. Adv. Sci. 5, (2018). source
- cites [20] Chang, Y. et al. All-Dielectric Surface-Enhanced Infrared Absorption-Based Gas Sensor Using Guided Resonance. ACS Appl. Mater. Interfaces 10, 38272–38279 (2018). source
- cites [21] Hu, H. et al. Gas identification with graphene plasmons. Nat. Commun. 10, 1131 (2019). source
- cites [22] Zhou, H. et al. Metal–Organic Framework‐Surface‐Enhanced Infrared Absorption Platform Enables Simultaneous On‐Chip Sensing of Greenhouse Gases. Adv. Sci. 7, 2001173 (2020). source
- cites [23] Nuñez, J., Boersma, A., Grand, J., Mintova, S. & Sciacca, B. Thin Functional Zeolite Layer Supported on Infrared Resonant Nano‐Antennas for the Detection of Benzene Traces. Adv. Funct. Mater. 31, 2101623 (2021). source
- cites [24] Yoo, D. et al. Low-Power Optical Trapping of Nanoparticles and Proteins with Resonant Coaxial Nanoaperture Using 10 nm Gap. Nano Lett. 18, 3637–3642 (2018). source
- cites [25] Miao, X., Yan, L., Wu, Y. & Liu, P. Q. High-sensitivity nanophotonic sensors with passive trapping of analyte molecules in hot spots. Light Sci. Appl. 10, 5 (2021). source
- cites [26] Hwang, I. et al. Plasmon-Enhanced Infrared Spectroscopy Based on Metamaterial Absorbers with Dielectric Nanopedestals. ACS Photonics 5, 3492–3498 (2018). source
- Also stated in: § Design principles of hook nanoantenna · plain
Probably not a substantial claim. The paper may give no reason why it holds.
Interpretationnone givenWMHNA integrated into a microfluidic system is compatible with biomolecular systems for molecular detection in the aqueous environment.
“To demonstrate the capability of liquid dynamics monitoring with broadband fingerprint absorption, we integrate WMHNA into a microfluidic system (Fig. 5a), which is compatible with biomolecular systems for molecular detection in the aqueous environment.”
Quote in the paperNot a substantial claimProbably not a substantial claim.
InterpretationreasoningA slight change of intensity of light is observed according to Beer-Lambert's law.
“Therefore, a slight change of intensity of light is observed according to Beer-Lambert’s law.”
Quote in the paperSubstantial claimIt may state two separate claims as one.
PremisereasoningMethanol, ethanol, and isopropanol have the same functional group of hydroxy and methyl bond, resulting in similar absorption spectra in 6 μm to 9 μm wavelengths.
“Both of the molecules have the same functional group of hydroxy and methyl bond, resulting in similar absorption spectra in 6 μm to 9 μm wavelengths.”
Quote in the paperSubstantial claimProbably not a substantial claim. The paper’s conclusions may not depend on it.
Interpretationnone givenThe order of PC represents the degree of difference between each spectrum.
“The order of PC represents the degree of difference between each spectrum.”
Quote in the paperNot a substantial claimProbably not a substantial claim.
PremisecitationPrevious works show many solutions using multiple resonances of nanoantenna structures to capture plasmonic enhanced molecular fingerprints in separate wavelength ranges for sensing different molecule species.
“Previous works show many solutions using multiple resonances of nanoantenna structures to capture the plasmonic enhanced molecular fingerprints in separate wavelength ranges for sensing different molecule species22,39,41,50,54–56.”
Quote in the paperSubstantial claim0 of 7 sources checked- cites [22] Zhou, H. et al. Metal–Organic Framework‐Surface‐Enhanced Infrared Absorption Platform Enables Simultaneous On‐Chip Sensing of Greenhouse Gases. Adv. Sci. 7, 2001173 (2020). source
- cites [39] Rodrigo, D. et al. Resolving molecule-specific information in dynamic lipid membrane processes with multi-resonant infrared metasurfaces. Nat. Commun. 9, (2018). source
- cites [41] Rodrigo, D., Tittl, A., John-Herpin, A., Limaj, O. & Altug, H. Self-Similar Multiresonant Nanoantenna Arrays for Sensing from Near- to Mid-Infrared. ACS Photonics 5, 4903–4911 (2018). source
- cites [50] John‐Herpin, A., Kavungal, D., Mücke, L. & Altug, H. Infrared Metasurface Augmented by Deep Learning for Monitoring Dynamics between All Major Classes of Biomolecules. Adv. Mater. 33, 2006054 (2021). source
- cites [54] Hasan, D. et al. Thermoplasmonic Study of a Triple Band Optical Nanoantenna Strongly Coupled to Mid IR Molecular Mode. Sci. Rep. 6, 22227 (2016). source
- cites [55] Aslan, E. et al. Multispectral Cesaro-Type Fractal Plasmonic Nanoantennas. ACS Photonics 3, 2102–2111 (2016). source
- cites [56] Ren, Z., Dang, Z. & Lee, C. Multi-Band Mid-IR Molecules Identification Using Plasmonic Metamaterials Induced by Bright-Dark Coupling. in 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS) vols 2020-Janua 725–728 (IEEE, 2020). source
It may state two separate claims as one.
Interpretationown dataWith the help of ML, the IR spectrum of HNA with different molecules can be reduced to three principal components, which indicate the three key features of loading effect, wavelength shifts, and enhanced fingerprint absorption.
“With the help of ML, the IR spectrum of HNA with different molecules can be reduced to three principal components, which indicate the three key features of loading effect, wavelength shifts, and enhanced fingerprint absorption.”
Quote in the paperSubstantial claim
The authors state this as an assumption.
There is a need for new nanoantenna structures for continuous broadband detection and new methods to distinguish chemically similar molecules from partially overlapped absorption spectra.
“Therefore, new nanoantenna structures need to be developed for continuous broadband detection to capture multiple absorption peaks in fingerprint regions and new methods need to be conducted to distinguish the chemically similar molecules from the partially overlapped absorption spectra.”
The paper relies on this without support.
Molecules of the same class have strong overlap in fingerprint absorption due to similar chemical structure.
“However, since the same classes of molecules always have a strong overlap in fingerprint absorption due to the similar chemical structure, it is challenging to distinguish the same classes of molecules in small concentrations by monitoring single absorption peak of each molecules.”
The paper relies on this without support.
In the IR fingerprint region, plasmonic nanoantenna signals of absorption changes and wavelength shifts always occur together.
“Unfortunately, in the IR fingerprint region, the PNA signal of absorption changes and wavelength shifts always comes together, hindering the usage of complementary information about the analyte’s physical and chemical properties for molecule identification.”
The authors state this as an assumption.
The temporal coupled-mode theory model accurately describes the plasmon-molecule coupling system.
“To understand the design principles, we use the TCMT model52 to analyze the plasmon-molecules coupling system.”
The authors state this as an assumption.
The Lorentz model accurately represents the optical properties of analytes.
“To demonstrate the individual response caused by three effects, we use finite-difference time-domain (FDTD) methods to simulate the antenna response and manipulate the optical properties (refractive index, n and absorption coefficient, k) of analytes by Lorentz model, which has been used in our previous works19,51”
The authors state this as an assumption.
In the TCMT model, the plasmonic resonance is treated as a bright mode coupled to incident light, and molecular vibration is treated as a dark mode whose coupling efficiency is much lower than the plasmonic nanoantenna and can be ignored.
“We treat the plasmonic resonant (denoted as P) as a bright mode that is coupled to the incident light, while we treat the molecular vibration (denoted as M) as a dark mode, in which coupling efficiency is much lower than PNA and can be ignored in their coupling system.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Ren et al. designed a hook-shaped nanoantenna array that utilizes wavelength multiplexing for continuous broadband detection of multiple absorption peaks in the fingerprint region.
“Ren et al. (Ren et al. 2022) designed a hook-shaped nanoantenna array that utilizes wavelength multiplexing for continuous broadband detection of multiple absorption peaks in the fingerprint region (Fig. 8c-i).”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Ren et al. 2022
Through integration of PCA and SVM algorithms, Ren et al. achieved 100% recognition accuracy for identification of methanol, ethanol, and isopropanol.
“However, through the integration of PCA and SVM algorithms, the authors have achieved 100% recognition accuracy for the identification of methanol, ethanol, and isopropanol, as demonstrated in Fig. 8c-iii-vi.”
From Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform (2023) · cites this paper as Ren et al. 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Silk-and-PMMA results are assigned to the wrong figure panel
MinorThe text points to Fig. 4b for silk-and-PMMA sensing results, whereas the caption identifies Fig. 4b as baseline-corrected spectra from panel a and assigns silk-and-PMMA sensing to Fig. 4e.
“Figure 4b shows the sensing results of silk and PMMA on HNA supercell with the broadband response from ~5.5 μm to ~8.5 μm.”
“b The baseline-corrected reflection spectra from (a). c The normalized sensitivity of hook nanoantenna devices with different L indicates the loading effect. Sensitivity becomes maximum when the two wavelengths of molecular vibration and antenna resonance are matched. d the reflection spectra of WHHNA and hook nanoantenna devices (P1, P8, and P16) to form the supercell. e The sensing characterization of WMHNA by two types of thin films of PMMA and silk.”
Radiative and absorptive loss symbols are reversed
MinorThe initial definition makes γa the radiative loss and γr the absorptive loss. The later design explanation treats γa as ohmic loss and γr as the loss governing radiation.
“γa and γr denote the radiative and absorptive losses of HNA, while γm is the absorptive loss of molecules.”
“The γa is related to the ohmic loss of plasmonic material (e.g., Au in this work) and is almost the same among different antenna structures. Therefore, the philosophy to use hook shape in nanoantenna design is to engineer γr to tune the radiation from electron oscillation by inducing inverse current from dipole resonance.”
Classification samples are called gases instead of liquids
MinorThe classification section says the binary classifiers distinguish six gases, but the described samples are aqueous alcohol solutions and their mixtures.
“Because SVM is a classification algorithm for two-group classification problems, we transform it into a set of binary classification problems to distinguish 6 different gases.”
“We designed a series of experiments to characterize the recognition capability of WMHNA using 1% methanol, ethanol, and IPA in water and mixture sets of each two in the same volume.”
First two principal components are assigned opposite effects
MinorThe Fig. 6 caption assigns the antenna loading effect to PC 1 and the refractometric wavelength shift to PC 2. The Results text reverses those assignments, leaving the interpretation of the classification features inconsistent.
“the first principal component (PC 1) represents the antenna loading effect of water absorption peaks at 6.0 μm. The second principal component (PC 2) represents the wavelength shift of WMHNA due to the refractive index of the analyte.”
“The first PC represents the wavelength shift of HNA resonance by RE and the second PC represents the modulation of water absorption peak by ALE.”
Citations
Cites
Temporal coupled-mode theory for the Fano resonance in optical resonators
Fan, S., Suh, W., & Joannopoulos, J. D. (2003) · Journal of the Optical Society of America A · doi:10.1364/josaa.20.000569 · Cited by 1,700
Mid-infrared plasmonic biosensing with graphene
Rodrigo, D., Limaj, O., Janner, D., Etezadi, D., García de Abajo, F. J., Pruneri, V., & Altug, H. (2015) · Science · doi:10.1126/science.aab2051 · Cited by 1,462
Eilers, P. H. C. (2003) · Analytical Chemistry · doi:10.1021/ac034173t · Cited by 1,412
Imaging-based molecular barcoding with pixelated dielectric metasurfaces
Tittl, A., Leitis, A., Liu, M., Yesilköy, F., Choi, D., Neshev, D. N., Kivshar, Y. S., & Altug, H. (2018) · Science · doi:10.1126/science.aas9768 · Cited by 1,351
Qiu, G., Gai, Z., Tao, Y., Schmitt, J., Kullak-Ublick, G. A., & Wang, J. (2020) · ACS Nano · doi:10.1021/acsnano.0c02439 · Cited by 1,059
Mid-Infrared Plasmonic Biosensing with Graphene
Rodrigo, D., Limaj, O., Janner, D., Etezadi, D., García de Abajo, F. J., Pruneri, V., & Altug, H. (2015) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 898
Resonant Plasmonic and Vibrational Coupling in a Tailored Nanoantenna for Infrared Detection
Neubrech, F., Pucci, A., Cornelius, T. W., Karim, S., García-Etxarri, A., & Aizpurua, J. (2008) · Physical Review Letters · doi:10.1103/physrevlett.101.157403 · Cited by 743
Ultra-sensitive vibrational spectroscopy of protein monolayers with plasmonic nanoantenna arrays
Adato, R., Yanik, A. A., Amsden, J. J., Kaplan, D. L., Omenetto, F. G., Hong, M. K., Erramilli, S., & Altug, H. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907459106 · Cited by 668
Angle-multiplexed all-dielectric metasurfaces for broadband molecular fingerprint retrieval
Leitis, A., Tittl, A., Liu, M., Lee, B. H., Gu, M. B., Kivshar, Y. S., & Altug, H. (2019) · Science Advances · doi:10.1126/sciadv.aaw2871 · Cited by 550
A wearable patch for continuous analysis of thermoregulatory sweat at rest
Nyein, H. Y. Y., Bariya, M., Tran, B., Ahn, C. H., Brown, B. J., Ji, W., Davis, N., & Javey, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22109-z · Cited by 437
Fluorinated MOF platform for selective removal and sensing of SO2 from flue gas and air
Tchalala, M. R., Bhatt, P. M., Chappanda, K. N., Tavares, S. R., Adil, K., Belmabkhout, Y., Shkurenko, A., Cadiau, A., Heymans, N., Weireld, G. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09157-2 · Cited by 415
Adato, R., & Altug, H. (2013) · Nature Communications · doi:10.1038/ncomms3154 · Cited by 406
Muraviev, A. V., Smolski, V., Loparo, Z. E., & Vodopyanov, K. L. (2018) · Nature Photonics · doi:10.1038/s41566-018-0135-2 · Cited by 337
Non-dispersive infrared multi-gas sensing via nanoantenna integrated narrowband detectors
Tan, X., Zhang, H., Li, J., Wan, H., Guo, Q., Zhu, H., Liu, H., & Yi, F. (2020) · Nature Communications · doi:10.1038/s41467-020-19085-1 · Cited by 317
Jahani, Y., Arvelo, E. R., Yesilköy, F., Koshelev, K., Cianciaruso, C., Palma, M. D., Kivshar, Y. S., & Altug, H. (2021) · Nature Communications · doi:10.1038/s41467-021-23257-y · Cited by 308
Actively variable-spectrum optoelectronics with black phosphorus
Kim, H., Uddin, S. Z., Lien, D., Yeh, M., Azar, N. S., Balendhran, S., Kim, T., Gupta, N., Rho, Y., Grigoropoulos, C. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03701-1 · Cited by 291
Graphene acoustic plasmon resonator for ultrasensitive infrared spectroscopy
Lee, I., Yoo, D., Avouris, P., Low, T., & Oh, S. (2019) · Nature Nanotechnology · doi:10.1038/s41565-019-0363-8 · Cited by 276
Nanogapped Au Antennas for Ultrasensitive Surface-Enhanced Infrared Absorption Spectroscopy
Dong, L., Yang, X., Zhang, C., Cerjan, B., Zhou, L., Tseng, M. L., Zhang, Y., Alabastri, A., Nordlander, P., & Halas, N. J. (2017) · Nano Letters · doi:10.1021/acs.nanolett.7b02736 · Cited by 274
Rodrigo, D., Tittl, A., Ait-Bouziad, N., John-Herpin, A., Limaj, O., Kelly, C., Yoo, D., Wittenberg, N. J., Oh, S.-H., Lashuel, H. A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04594-x · Cited by 269
Baker, L. B., Model, J. B., Barnes, K. A., Anderson, M. L., Lee, S. P., Lee, K. A., Brown, S. D., Reimel, A. J., Roberts, T. J., Nuccio, R. P., et al. (2020) · Science Advances · doi:10.1126/sciadv.abe3929 · Cited by 243
Surface-Enhanced Infrared Spectroscopy Using Nanometer-Sized Gaps
Huck, C., Neubrech, F., Vogt, J., Toma, A., Gerbert, D., Katzmann, J., Härtling, T., & Pucci, A. (2014) · ACS Nano · doi:10.1021/nn500903v · Cited by 225
Gas identification with graphene plasmons
Hu, H., Yang, X., Guo, X., Khaliji, K., Biswas, S. R., García de Abajo, F. J., Low, T., Sun, Z., & Dai, Q. (2019) · Nature Communications · doi:10.1038/s41467-019-09008-0 · Cited by 225
Adato, R., Artar, A., Erramilli, S., & Altug, H. (2013) · Nano Letters · doi:10.1021/nl400689q · Cited by 224
Extraordinary performance of semiconducting metal oxide gas sensors using dielectric excitation
Potyrailo, R. A., Go, S., Sexton, D., Li, X., Alkadi, N., Kolmakov, A., Amm, B., St-Pierre, R., Scherer, B., Nayeri, M., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-0402-3 · Cited by 205
Infrared Plasmonic Biosensor for Real-Time and Label-Free Monitoring of Lipid Membranes
Limaj, O., Etezadi, D., Wittenberg, N. J., Rodrigo, D., Yoo, D., Oh, S.-H., & Altug, H. (2016) · Nano Letters · doi:10.1021/acs.nanolett.5b05316 · Cited by 196
Hybrid Metamaterial Absorber Platform for Sensing of CO 2 Gas at Mid‐IR
Hasan, D., & Lee, C. (2018) · Advanced Science · doi:10.1002/advs.201700581 · Cited by 173
Self-citationYoo, D., Gurunatha, K. L., Choi, H.-K., Mohr, D. A., Ertsgaard, C. T., Gordon, R., & Oh, S.-H. (2018) · Nano Letters · doi:10.1021/acs.nanolett.8b00732 · Cited by 173
Optical conductivity-based ultrasensitive mid-infrared biosensing on a hybrid metasurface
Zhu, Y., Li, Z., Hao, Z., DiMarco, C. S., Maturavongsadit, P., Hao, Y., Lu, M. C., Stein, A., Wang, Q., Hone, J., et al. (2018) · Light Science & Applications · doi:10.1038/s41377-018-0066-1 · Cited by 151
Metasurface‐Based Molecular Biosensing Aided by Artificial Intelligence
Tittl, A., John‐Herpin, A., Leitis, A., Arvelo, E. R., & Altug, H. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201901443 · Cited by 148
Nanoplasmonic mid-infrared biosensor for in vitro protein secondary structure detection
Etezadi, D., Warner IV, J. B., Ruggeri, F. S., Dietler, G., Lashuel, H. A., & Altug, H. (2017) · Light: Science & Applications · doi:10.1038/lsa.2017.29 · Cited by 137
Zhou, H., Hui, X., Li, D., Hu, D., Chen, X., He, X., Gao, L., Huang, H., Lee, C., & Mu, X. (2020) · Advanced Science · doi:10.1002/advs.202001173 · Cited by 136
Self-citationToward Healthcare Diagnoses by Machine-Learning-Enabled Volatile Organic Compound Identification
Zhu, J., Ren, Z., & Lee, C. (2020) · ACS Nano · doi:10.1021/acsnano.0c07464 · Cited by 130
Self-citationSelf-assembly of nanostructured glass metasurfaces via templated fluid instabilities
Gupta, T. D., Martin‐Monier, L., Yan, W., Bris, A. L., Nguyen-Dang, T., Page, A. G., Ho, K.-T., Yesilköy, F., Altug, H., Qu, Y., et al. (2019) · Nature Nanotechnology · doi:10.1038/s41565-019-0362-9 · Cited by 127
Mid-infrared metabolic imaging with vibrational probes
Shi, L., Liu, X., Shi, L., Stinson, H. T., Rowlette, J. A., Kahl, L. J., Evans, C. R., Zheng, C., Dietrich, L. E. P., & Min, W. (2020) · Nature Methods · doi:10.1038/s41592-020-0883-z · Cited by 126
John‐Herpin, A., Kavungal, D., von Mücke, L., & Altug, H. (2021) · Advanced Materials · doi:10.1002/adma.202006054 · Cited by 124
All-Dielectric Surface-Enhanced Infrared Absorption-Based Gas Sensor Using Guided Resonance
Chang, Y., Hasan, D., Dong, B., Wei, J., Ma, Y., Zhou, G., Ang, K. W., & Lee, C. (2018) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b16623 · Cited by 115
Self-citationLuong, H. M., Pham, M. T., Guin, T., Madhogaria, R. P., Phan, M.-H., Larsen, G. K., & Nguyen, T. D. (2021) · Nature Communications · doi:10.1038/s41467-021-22697-w · Cited by 109
Performance metrics and enabling technologies for nanoplasmonic biosensors
Oh, S., & Altug, H. (2018) · Nature Communications · doi:10.1038/s41467-018-06419-3 · Cited by 107
Extracellular vesicle drug occupancy enables real-time monitoring of targeted cancer therapy
Pan, S., Zhang, Y., Natalia, A., Lim, C. Z. J., Ho, N. R. Y., Chowbay, B., Loh, T. P., Tam, J. K. C., & Shao, H. (2021) · Nature Nanotechnology · doi:10.1038/s41565-021-00872-w · Cited by 103
Xu, J., Ren, Z., Dong, B., Liu, X., Wang, C., Tian, Y., & Lee, C. (2020) · ACS Nano · doi:10.1021/acsnano.0c05794 · Cited by 99
Self-citationHui, X., Yang, C., Li, D., He, X., Huang, H., Zhou, H., Chen, M., Lee, C., & Mu, X. (2021) · Advanced Science · doi:10.1002/advs.202100583 · Cited by 99
Self-citationAccessible Nearfields by Nanoantennas on Nanopedestals for Ultrasensitive Vibrational Spectroscopy
Çetin, A. E., Etezadi, D., & Altug, H. (2014) · Advanced Optical Materials · doi:10.1002/adom.201400171 · Cited by 95
Self-Similar Multiresonant Nanoantenna Arrays for Sensing from Near- to Mid-Infrared
Rodrigo, D., Tittl, A., John-Herpin, A., Limaj, O., & Altug, H. (2018) · ACS Photonics · doi:10.1021/acsphotonics.8b01050 · Cited by 85
Wei, J., Li, Y., Chang, Y., Hasan, D. M. N., Dong, B., Ma, Y., Qiu, C., & Lee, C. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b18002 · Cited by 81
Self-citationHuber, M., Kepesidis, K. V., Voronina, L., Božić‐Iven, M., Trubetskov, M. K., Harbeck, N., Krausz, F., & Žigman, M. (2021) · Nature Communications · doi:10.1038/s41467-021-21668-5 · Cited by 80
Luong, H. M., Pham, M. T., Guin, T. C., Madhogaria, R. P., Phan, M., Larsen, G. K., & Nguyen, T. D. (2021) · RePEc: Research Papers in Economics · Cited by 79
Le, T. H. H., & Tanaka, T. (2017) · ACS Nano · doi:10.1021/acsnano.7b02743 · Cited by 73
High-sensitivity nanophotonic sensors with passive trapping of analyte molecules in hot spots
Miao, X., Yan, L., Wu, Y., & Liu, P. Q. (2021) · Light: Science & Applications · doi:10.1038/s41377-020-00449-7 · Cited by 71
Kühner, L., Semenyshyn, R., Hentschel, M., Neubrech, F., Tarín, C., & Giessen, H. (2019) · ACS Sensors · doi:10.1021/acssensors.9b00488 · Cited by 68
Xu, J., Du, Y., Tian, Y., & Wang, C. (2020) · International Journal of Optomechatronics · doi:10.1080/15599612.2020.1857890 · Cited by 65
Plasmon-Enhanced Infrared Spectroscopy Based on Metamaterial Absorbers with Dielectric Nanopedestals
Hwang, I., Yu, J., Lee, J., Choi, J.-H., Choi, D.-G., Jeon, S., Lee, J., & Jung, J.-Y. (2018) · ACS Photonics · doi:10.1021/acsphotonics.8b00702 · Cited by 63
Semenyshyn, R., Hentschel, M., Stanglmair, C., Teutsch, T., Tarin, C., Pacholski, C., Giessen, H., & Neubrech, F. (2019) · Nano Letters · doi:10.1021/acs.nanolett.8b02372 · Cited by 60
Multispectral Cesaro-Type Fractal Plasmonic Nanoantennas
Aslan, E., Aslan, E., Wang, R., Hong, M. K., Erramilli, S., Turkmen, M., Saracoglu, O. G., & Dal Negro, L. (2016) · ACS Photonics · doi:10.1021/acsphotonics.6b00540 · Cited by 59
Hermans, J. J., Baij, L., Koenis, M. A. J., Keune, K., Iedema, P. D., & Woutersen, S. (2019) · Science Advances · doi:10.1126/sciadv.aaw3592 · Cited by 58
Plasmonic Mid-Infrared Filter Array-Detector Array Chemical Classifier Based on Machine Learning
Meng, J., Cadusch, J. J., & Crozier, K. B. (2021) · ACS Photonics · doi:10.1021/acsphotonics.0c01786 · Cited by 57
Determining the Interfacial Refractive Index via Ultrasensitive Plasmonic Sensors
Zhan, C., Liu, B.-W., Tian, Z.-Q., & Ren, B. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c01907 · Cited by 56
Le, T. H. H., Morita, A., Mawatari, K., Kitamori, T., & Tanaka, T. (2018) · ACS Photonics · doi:10.1021/acsphotonics.8b00398 · Cited by 45
Su, D.-S., Tsai, D. P., Yen, T.-J., & Tanaka, T. (2019) · ACS Sensors · doi:10.1021/acssensors.9b01225 · Cited by 39
Ultrasensitive Molecule Detection Based on Infrared Metamaterial Absorber with Vertical Nanogap
Hwang, I., Kim, M., Yu, J., Lee, J., Choi, J.-H., Park, S. A., Chang, W. S., Lee, J., & Jung, J. (2021) · Small Methods · doi:10.1002/smtd.202100277 · Cited by 39
Nanoscale Refractive Index Sensors with High Figures of Merit via Optical Slot Antennas
Gao, B., Wang, Y., Zhang, T., Xu, Y., He, A., Dai, L., & Zhang, J. (2019) · ACS Nano · doi:10.1021/acsnano.9b03406 · Cited by 25
Thermoplasmonic Study of a Triple Band Optical Nanoantenna Strongly Coupled to Mid IR Molecular Mode
Hasan, D., Ho, C. P., Pitchappa, P., Yang, B., Yang, C., & Lee, C. (2016) · Scientific Reports · doi:10.1038/srep22227 · Cited by 23
Self-citationHasan, D., Ho, C. P., & Lee, C. (2016) · ACS Omega · doi:10.1021/acsomega.6b00201 · Cited by 22
Self-citationNuñez, J., Boersma, A., Grand, J., Mintova, S., & Sciacca, B. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202101623 · Cited by 14
Ren, Z., Dang, Z., & Lee, C. (2020) · 2020 IEEE 33rd International Conference on Micro Electro Mechanical Systems (MEMS) · doi:10.1109/mems46641.2020.9056326 · Cited by 7
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Li, D., Yadav, A., Zhou, H., Roy, K., Thanasekaran, P., & Lee, C. (2024) · Global Challenges · doi:10.1002/gch2.202300244 · Cited by 437
Self-citationAI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring
Wasilewski, T., Kamysz, W., & Gębicki, J. (2024) · Biosensors · doi:10.3390/bios14070356 · Cited by 328
Wang, C., He, T., Zhou, H., Zhang, Z., & Lee, C. (2023) · Bioelectronic Medicine · doi:10.1186/s42234-023-00118-1 · Cited by 295
Self-citationZhou, H., Xu, L., Ren, Z., Zhu, J., & Lee, C. (2022) · Nanoscale Advances · doi:10.1039/d2na00608a · Cited by 145
Self-citationRecent Progress in Silicon‐Based Photonic Integrated Circuits and Emerging Applications
Xiao, Z., Liu, W., Xu, S., Zhou, J., Ren, Z., & Lee, C. (2023) · Advanced Optical Materials · doi:10.1002/adom.202301028 · Cited by 133
Self-citationOptical Metasurfaces for Biomedical Imaging and Sensing
Kim, H., Yun, H. K., Jeong, S., Lee, S., Cho, E., & Rho, J. (2025) · ACS Nano · doi:10.1021/acsnano.4c14751 · Cited by 114
Recent Advances in Artificial Intelligence Sensors
Zhang, Z., Wang, L., & Lee, C. (2023) · Advanced Sensor Research · doi:10.1002/adsr.202200072 · Cited by 107
Self-citationKavungal, D., Magalhães, P., Kumar, S. T., Rajasekhar, K., Lashuel, H. A., & Altug, H. (2023) · Science Advances · doi:10.1126/sciadv.adg9644 · Cited by 95
Zhang, Z., Liu, X., Zhou, H., Xu, S., & Lee, C. (2023) · Small Structures · doi:10.1002/sstr.202300325 · Cited by 99
Self-citationZhou, J., Dong, J., Hou, H., Huang, L., & Li, J. (2024) · Lab on a Chip · doi:10.1039/d3lc01012k · Cited by 94
Silicon Photonic Phase Shifters and Their Applications: A Review
Sun, H., Qiao, Q., Guan, Q., & Zhou, G. (2022) · Micromachines · doi:10.3390/mi13091509 · Cited by 88
Multifunctional Terahertz Biodetection Enabled by Resonant Metasurfaces
Wang, R., Ruan, H., Li, D., Huang, L., Jiang, R., Zhang, X., Yang, X., Wang, L., Wang, S., Kivshar, Y. S., et al. (2025) · Advanced Materials · doi:10.1002/adma.202418147 · Cited by 85
Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism
Xu, C., Ren, Z., Zhou, H., Zhou, J., Ho, C. P., Wang, N., & Lee, C. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01186-3 · Cited by 84
Self-citationMultidimensional detection enabled by twisted black arsenic–phosphorus homojunctions
Wang, F., Zhu, S., Chen, W., Han, J., Duan, R., Wang, C., Dai, M., Sun, F., Jin, Y., & Wang, Q. J. (2024) · Nature Nanotechnology · doi:10.1038/s41565-023-01593-y · Cited by 81
He, T., Wen, F., Yang, Y., Le, X., Liu, W., & Lee, C. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c04527 · Cited by 75
Self-citationSmart mid-infrared metasurface microspectrometer gas sensing system
Meng, J., Balendhran, S., Sabri, Y. M., Bhargava, S. K., & Crozier, K. B. (2024) · Microsystems & Nanoengineering · doi:10.1038/s41378-024-00697-2 · Cited by 67
Zhou, J., Zhang, Z., Dong, B., Ren, Z., Liu, W., & Lee, C. (2022) · ACS Nano · doi:10.1021/acsnano.2c10163 · Cited by 65
Self-citationElectrochemical Sensor to Detect Antibiotics in Milk Based on Machine Learning Algorithms
Aliev, T. A., Belyaev, V. E., Pomytkina, A. V., Nesterov, P. V., Shityakov, S., Sadovnichii, R. V., Novikov, A. S., Orlova, O. Y., Masalovich, M. S., & Скорб, Е. В. (2023) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.3c12050 · Cited by 63
Sun, Z., He, T., Ren, Z., Wang, C., Liu, X., Zhang, Z., Zhou, J., Guo, X., Yang, Y., & Lee, C. (2025) · SmartSys · doi:10.1002/sys3.4 · Cited by 60
Self-citationZhou, H., Li, D., Lv, Q., & Lee, C. (2025) · Chemical Society Reviews · doi:10.1039/d4cs00427b · Cited by 58
Self-citationOver-coupled resonator for broadband surface enhanced infrared absorption (SEIRA)
Paggi, L., Fabas, A., Ouazzani, H. E., Hugonin, J., Fayard, N., Bardou, N., Dupuis, C., Greffet, J., & Bouchon, P. (2023) · Nature Communications · doi:10.1038/s41467-023-40511-7 · Cited by 56
Passive trapping of biomolecules in hotspots with all-dielectric terahertz metamaterials
Lin, T., Huang, Y., Shuncong Zhong, A., Shi, T., Sun, F., Zhong, Y., Zeng, Q., Zhang, Q., & Cui, D. (2024) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2024.116126 · Cited by 53
Guo, X., Zhang, Z., Ren, Z., Li, D., Xu, C., Wang, L., Liu, W., Zhuge, Y., Zhou, G., & Lee, C. (2025) · Advanced Materials · doi:10.1002/adma.202510417 · Cited by 57
Self-citationLi, D., Xu, C., Xie, J., & Lee, C. (2023) · Nanomaterials · doi:10.3390/nano13162377 · Cited by 51
Self-citationLi, D., Zhou, H., Chen, Z., Ren, Z., Xu, C., He, X., Liu, T., Chen, X., Huang, H., Lee, C., et al. (2023) · Advanced Materials · doi:10.1002/adma.202301787 · Cited by 50
Self-citationLi, D., Zhou, H., Ren, Z., & Lee, C. (2025) · Small Science · doi:10.1002/smsc.202400250 · Cited by 48
Self-citationZhu, J., Ji, S., Ren, Z., Wu, W., Zhang, Z., Ni, Z., Liu, L., Zhang, Z., Song, A., & Lee, C. (2023) · Nature Communications · doi:10.1038/s41467-023-38200-6 · Cited by 48
Self-citationSurface plasmons-phonons for mid-infrared hyperspectral imaging
Zhou, H., Li, D., Ren, Z., Xu, C., Wang, L., & Lee, C. (2024) · Science Advances · doi:10.1126/sciadv.ado3179 · Cited by 45
Self-citationAdvances in olfactory augmented virtual reality towards future metaverse applications
Zhang, Z., Guo, X., & Lee, C. (2024) · Nature Communications · doi:10.1038/s41467-024-50261-9 · Cited by 45
Self-citationZhou, H., Ren, Z., Li, D., Xu, C., Mu, X., & Lee, C. (2023) · Nature Communications · doi:10.1038/s41467-023-43127-z · Cited by 44
Self-citationXu, S., Ren, Z., Dong, B., Zhou, J., Liu, W., & Lee, C. (2022) · Advanced Optical Materials · doi:10.1002/adom.202202228 · Cited by 41
Self-citationDevelopment of Photonic In-Sensor Computing Based on a Mid-Infrared Silicon Waveguide Platform
Liu, X., Zhang, Z., Zhou, J., Liu, W., Zhou, G., & Lee, C. (2024) · ACS Nano · doi:10.1021/acsnano.4c04052 · Cited by 38
Self-citationXu, C., Ren, Z., Zhou, H., Zhou, J., Li, D., & Lee, C. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202314482 · Cited by 36
Self-citationXia, L., Liu, Y., Chen, R. T., Weng, B., & Zou, Y. (2024) · Applied Physics Reviews · doi:10.1063/5.0197236 · Cited by 38
Xie, J., Ren, Z., Wei, J., Liu, W., Zhou, J., & Lee, C. (2023) · Advanced Optical Materials · doi:10.1002/adom.202202867 · Cited by 36
Self-citationGajbhiye, P. A., SINGH, S. P., & Sharma, M. K. (2025) · Discover Electronics · doi:10.1007/s44291-025-00084-9 · Cited by 34
Zhou, H., Li, D., & Lee, C. (2025) · AI Sensors · doi:10.3390/aisens1010005 · Cited by 37
Self-citationPlasmonic Biosensors with Nanostructure for Healthcare Monitoring and Diseases Diagnosis
An, T., Wen, J., Dong, Z., Zhang, Y., Zhang, J., Qin, F., Wang, Y., & Zhao, X. (2022) · Sensors · doi:10.3390/s23010445 · Cited by 32
Mapping information and light: Trends of AI-enabled metaphotonics
Lee, S., Park, C. G., & Rho, J. (2024) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2024.101144 · Cited by 33
Terahertz molecular vibrational sensing using 3D printed anapole meta-biosensor
Yang, C., Shen, Z., Cui, Y., Zhang, N., Zhang, L., Yan, R., & Chen, X. (2025) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2025.117351 · Cited by 31
MEMS enabled suspended silicon waveguide platform for long-wave infrared modulation applications
Liu, X., Qiao, Q., Dong, B., Liu, W., Xu, C., Xu, S., & Zhou, G. (2022) · International Journal of Optomechatronics · doi:10.1080/15599612.2022.2137608 · Cited by 29
Self-citationMa, Y., Liu, W., Liu, X., Wang, N., & Zhang, H. (2024) · International Journal of Optomechatronics · doi:10.1080/15599612.2024.2342279 · Cited by 29
A hybrid graphene metamaterial absorber for enhanced modulation and molecular fingerprint retrieval
Chen, Z., Li, D., Zhou, H., Liu, T., & Mu, X. (2023) · Nanoscale · doi:10.1039/d3nr02830e · Cited by 28
Hybrid Plasmonic Modes for Enhanced Refractive Index Sensing
Dana, B. D., Ji, B., Lin, J., Li, L., Koya, A. N., & Li, W. (2023) · Advanced Sensor Research · doi:10.1002/adsr.202300066 · Cited by 27
Intelligent understanding of spectra: from structural elucidation to property design
Feng, S., Huang, M., Li, Y., Cai, A., Yue, X., Wang, S., Chen, L., Jiang, J., & Luo, Y. (2025) · Chemical Society Reviews · doi:10.1039/d4cs01293c · Cited by 27
Vibrational Coupling to Quasi‐Bound States in the Continuum under Tailored Coupling Conditions
Watanabe, K., Devi, H. R., Iwanaga, M., & Nagao, T. (2023) · Advanced Optical Materials · doi:10.1002/adom.202301912 · Cited by 25
Spectral programmable mid-infrared optical parametric oscillator
Liang, J., Xu, J., Ke, Y., Li, S. K., He, J., Guo, Y., Zhang, Y., Ma, X., Ye, J., Li, X., et al. (2024) · Photonics Research · doi:10.1364/prj.524117 · Cited by 24
Ren, Z., Zhang, Z., Zhuge, Y., Xiao, Z., Xu, S., Zhou, J., & Lee, C. (2025) · Nano-Micro Letters · doi:10.1007/s40820-025-01743-y · Cited by 26
Self-citationLi, D., Zhou, H., Ren, Z., Xu, C., & Lee, C. (2024) · Nano-Micro Letters · doi:10.1007/s40820-024-01520-3 · Cited by 26
Self-citationMultimodal In‐Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor
Xiao, Z., Ren, Z., Zhuge, Y., Zhang, Z., Zhou, J., Xu, S., Xu, C., Dong, B., & Lee, C. (2024) · Advanced Science · doi:10.1002/advs.202408597 · Cited by 25
Self-citationWang, J., Xie, Z., Zhu, Y., Zeng, P., He, S., Wang, J., Wei, H., & Yu, C. (2024) · Trends in Environmental Analytical Chemistry · doi:10.1016/j.teac.2024.e00226 · Cited by 24
Transformation of peptides to small molecules in medicinal chemistry: Challenges and opportunities
Han, Z.-Y., Shen, Z., Pei, J., You, Q.-D., Zhang, Q.-Y., & Wang, L. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.06.019 · Cited by 21
Niu, Q., He, K., Zhao, R. J., Xie, Y., Murtaza, G., Zhang, G., Xiao, S., Zeng, F., Yang, Y., & Zhu, J. (2025) · ACS Photonics · doi:10.1021/acsphotonics.5c01303 · Cited by 23
Xie, J., Ren, Z., Zhou, H., Zhou, J., Liu, W., & Lee, C. (2024) · Advanced Optical Materials · doi:10.1002/adom.202401582 · Cited by 21
Self-citationMetasurface biosensors: Status and prospects
Iwanaga, M., Hu, Q., & Tang, Y. (2025) · Applied Physics Reviews · doi:10.1063/5.0253333 · Cited by 23
Rotation-Symmetry Grating Contact Photodetector for Visible Full Linear Polarimetry Detection
Wang, C., He, C., Liu, L., Tang, Z., Wang, Y., Wang, H., Liu, W., Wang, X., Wang, X., & Pan, A. (2025) · Nano Letters · doi:10.1021/acs.nanolett.5c00461 · Cited by 19
Plasmonic nanosensors for environmental pollutants sensing: recent advances and perspectives
Güler, K. Ç., Göktürk, I., Yılmaz, F., Araz, A., & Deni̇zli̇, A. (2024) · Essential Chem · doi:10.1080/28378083.2024.2386522 · Cited by 20
Dai, X., Chu, Z., Deng, J., Li, F., Zhou, J., Xiong, D., Zhou, X., Chen, X., Li, N., Li, Z., et al. (2022) · Optics Express · doi:10.1364/oe.472051 · Cited by 18
Substrate‐Free Terahertz Metamaterial Sensors With Customizable Configuration and High Performance
Lin, T., Zeng, Q., Huang, Y., Shuncong Zhong, A., Shi, T., Zhong, Y., Sun, F., & Zhang, Q. (2024) · Advanced Optical Materials · doi:10.1002/adom.202400689 · Cited by 18
Wavelength multiplexing infrared metasurfaces for protein recognition and trace detection
Dong, S., Dong, C., Shen, K., Zheng, Y., Sun, J., Cheng, Z., Hu, H., Zhang, F., Zhang, Z., Liu, H., et al. (2023) · Nanophotonics · doi:10.1515/nanoph-2023-0517 · Cited by 16
Self-citationWei, Y., Si, L., Han, K., Xu, H., Bao, X., & Zhu, W. (2025) · Nanoscale · doi:10.1039/d4nr04665j · Cited by 15
Inverse design of refractory mid-wave infrared narrowband thermal emitters for optical gas sensing
Li, X., Wen, Z., Zhou, D., Zhang, J., Sun, Y., Xu, H., Shi, X., Cao, D., Zhang, Y., Dai, N., et al. (2023) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2023.101687 · Cited by 13
Ram Prakash, S., Kumar, R., & Mitra, A. (2024) · ACS Photonics · doi:10.1021/acsphotonics.4c00276 · Cited by 15
Liao, J., Wang, P., Fu, Q., Dai, S., Chen, W., Zhang, D., Deng, L., Li, J., Dai, T., & Yang, J. (2024) · Optics Express · doi:10.1364/oe.542260 · Cited by 13
Polarization‐Sensitive Momentum‐Matching Interlayer Excitons for Infrared Photodetection
Che, Z., Deng, W., Li, J., Zhao, C., Wang, F., Wu, Y., Wang, Q. J., Qiu, C., & Zhang, Y. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202416994 · Cited by 13
Multicolor Biosensors for Early Diagnosis of Diseases
Aghili, S., Abbariki, N., Daneshgar, H., Edrisi, M., & Rabiee, N. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c04693 · Cited by 13
Surface Enhanced Infrared Absorption Using Single Conducting Polymer Antennas
Li, X., Shu, Z., Zhu, G., Wang, J., Ding, Y., Du, W., & Wang, T. (2024) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.4c00421 · Cited by 12
A self-aligned assembling terahertz metasurface microfluidic sensor for liquid detection
Cao, Y., Sun, H., Chen, Y., Ma, L., Li, L., Jin, S., & Wu, W. (2024) · Nanoscale · doi:10.1039/d4nr00466c · Cited by 12
Quasi-bound states-driven terahertz molecular fingerprint retriever
Yang, C., Shen, Z., Han, D., Hou, Y., Ji, X., Zhang, L., Yan, R., & Chen, X. (2024) · Optics & Laser Technology · doi:10.1016/j.optlastec.2024.112139 · Cited by 12
Rosas, S., Biswas, S. K., Adi, W., Kuruoğlu, F., Beisenova, A., Patankar, M. S., & Yesilköy, F. (2025) · Advanced Materials · doi:10.1002/adma.202504355 · Cited by 12
Zhu, J., Xiao, Y., Huang, X., Niu, Q., Zeng, L., Lin, S., Jiang, M., Huang, T., Chen, H., Xie, Y., et al. (2025) · Advanced Science · doi:10.1002/advs.202511337 · Cited by 13
Self-citationKang, M., Choi, S., Fu, K., Liu, X., Wei, Z., Jin, L., Wang, H., Martin, O. J. F., Yang, J. K. W., So, S., et al. (2026) · Opto-Electronic Advances · doi:10.29026/oea.2026.250263 · Cited by 11
Jurkūnas, M., Talaikis, M., Klimkevičius, V., Pudžaitis, V., Niaura, G., & Makuška, R. (2024) · Langmuir · doi:10.1021/acs.langmuir.3c03925 · Cited by 11
Structurally‐Modulated Substrate of MXene for Surface‐Enhanced Raman Scattering Sensing
Gao, Z., & Lai, W. (2024) · ChemPhysChem · doi:10.1002/cphc.202400604 · Cited by 10
Hwang, H., Ko, K., Nurrahman, M. R., Moon, K., Ju, J. J., Han, S.-W., Jung, H., Seo, M., & Lee, H. (2025) · APL Photonics · doi:10.1063/5.0235751 · Cited by 9
Wen, Y., Zhu, H., Song, F., Chen, R., Zheng, C., Pi, M., Zhang, Y., & Wang, Y. (2025) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2025.126621 · Cited by 10
Bio-Inspired Nanomembranes as Building Blocks for Nanophotonics, Plasmonics and Metamaterials
Jakšić, Z., Obradov, M., & Jaksic, O. M. (2022) · Biomimetics · doi:10.3390/biomimetics7040222 · Cited by 11
Qi, Y., Zhang, Y., Tang, S., & Zeng, Z. (2025) · Forests · doi:10.3390/f16010186 · Cited by 11
Luo, S., Ding, Y., Pan, M., Zhou, R., Han, H., Liao, Z., & Chen, D. (2024) · Optics & Laser Technology · doi:10.1016/j.optlastec.2024.111685 · Cited by 8
Ren, Z., Yin, H., Zhang, C., Fang, M., Lu, Y. Q., Sun, X. Y., He, J., & Liu, X. (2025) · Nano Research · doi:10.26599/nr.2025.94907833 · Cited by 8
Self-citationSun, W., Yang, F., Shi, X., Dong, Y., Dai, R., Jia, Y. M., Xin, W., Li, J. H., Wu, Z., & Liang, Z. (2025) · Applied Physics Letters · doi:10.1063/5.0276130 · Cited by 7
Dong, S., Yang, D., Wang, Q., Hu, H., Sun, J., Shen, K., Dong, C., Liu, H., Zhu, Z., & Lu, H. (2025) · Chinese Optics Letters · doi:10.3788/col202523.053602 · Cited by 7
Zhong, S., Guan, Z., Yang, F., Jiang, Y., Zhao, L., Wang, W., Liu, D., Cai, W., & Li, Y. (2024) · Nano Letters · doi:10.1021/acs.nanolett.4c01562 · Cited by 6
AI‐Enabled Inverse Design and Molecular Identification Using Phase Change Metamaterial Absorber
Ram Prakash, S., Jain, A., Kumar, R., & Mitra, A. (2024) · Advanced Optical Materials · doi:10.1002/adom.202402407 · Cited by 7
Dynamic Plasmonic Full‐Color Generation via Machine Learning and Liquid Crystals
Li, K., Yuan, S., Zhang, J., Tian, Y., Li, J., You, B., & Zhu, X. (2025) · Laser & Photonics Review · doi:10.1002/lpor.202401979 · Cited by 5
An, D., Liu, Z., Tang, Z., Ni, J., Wang, Q. J., & Yu, X. (2024) · Laser & Photonics Review · doi:10.1002/lpor.202400871 · Cited by 5
Joshi, R., Ahmad, R., Gardner, K., Ahmadi, H., Chen, C., Stott, S. L., & Li, W. (2025) · Biomaterials Science · doi:10.1039/d5bm00577a · Cited by 5
Comprehensive Phase Shifter Review: State of the Art and Future Trends
Gharsalli, S., Aloui, R., Mhatli, S., Corona‐Chávez, A., Brito‐Brito, Z., Mishra, S. K., & Llamas-Garro, I. (2025) · Progress In Electromagnetics Research B · doi:10.2528/pierb25082704 · Cited by 4
Li, D., Chen, Z., Wu, X., Liu, T., Zhang, G., Mu, X., & Lee, C. (2026) · ACS Nano · doi:10.1021/acsnano.5c20156 · Cited by 4
Self-citationSpiesshofer, N., Wyatt, E. W., Sztranyovszky, Z., Todd, C., Mykytiuk, T. V., Beattie, J. W., Davies, R., Arul, R., Lindo, V., Krauss, T. F., et al. (2025) · Optica · doi:10.1364/optica.567324 · Cited by 4
Liu, T., Yu, S., Liu, H., Li, X., Ju, J., & Zou, S. (2024) · Infrared Physics & Technology · doi:10.1016/j.infrared.2024.105506 · Cited by 4
Jeon, T., Nateghi, A., Choi, C., Jewell, J. L., & Scherer, A. (2024) · Advanced Optical Materials · doi:10.1002/adom.202302241 · Cited by 3
Hu, Y., Zheng, Z., Ma, H., Zhu, S., Yu, Y., Hong, J., Tang, W., He, J., Zhang, L., Liu, C., et al. (2025) · Nanomaterials · doi:10.3390/nano15040284 · Cited by 3
MEMS-Driven Electrically Reconfigurable Platform for Photonic Quasi-Bound States in the Continuum
Zhou, H., Chen, T.-Y., Ren, Z., Li, D., Xu, C., Tsai, C., Li, W., & Lee, C. (2025) · International Conference on Solid-State Sensors, Actuators and Microsystems · doi:10.1109/transducers61432.2025.11109224 · Cited by 2
Self-citationTopologically enhanced guided acoustic wave sensors
Maggioli, T., Pantuso, N. S., Galli, M., Ponti, J. M. D., Kaya, O., Ghosh, S., Colangelo, M., & Cassella, C. (2025) · Physical Review Applied · doi:10.1103/l4y3-14dx · Cited by 2
MEMS-enabled reconfigurable metasurface and nanophotonics: advances and perspectives
Li, D., Liu, W., Wang, Y., Zheng, D., & Lee, C. (2026) · Micro & Nano Manufacturing · doi:10.1007/s44374-026-00015-y · Cited by 2
Self-citationPolarization-Dependent Dual-Mode Surface Enhanced Infrared Absorption Laser Spectroscopy
Ni, J., An, D., Tang, Z., Bian, Y., Wang, Q., & Yu, X. (2024) · IEEE Photonics Technology Letters · doi:10.1109/lpt.2024.3412132 · Cited by 2
Nurrahman, M. R., Kim, D., Jeong, K., Kim, K., Lee, C., & Seo, M. (2023) · Optics Letters · doi:10.1364/ol.489424 · Cited by 2
Self-citationWaveguide Integrated Gradient Nanoantenna for Broadband Surface-enhanced Infrared Spectroscopy
An, D., Ni, J., Tang, Z., Yu, X., & Song, Q. (2023) · Photonics Global Conference · doi:10.1109/pgc60057.2023.10343716 · Cited by 2
Broadband tunable nanostructures for surface-enhanced infrared absorption spectroscopy
Ni, J., Liu, Z., Tang, Z., Wang, C. Y., Wang, Q., & Yu, X. (2025) · Optics Express · doi:10.1364/oe.568176 · Cited by 1
Metadata
- Authors
- Ren, Z., Zhang, Z., Wei, J., Dong, B., & Lee, C.
- Affiliations
- National University of Singapore (SG)
- Corresponding author
- Lee, Chengkuo
- Venue
- Nature Communications
- Published
- 2022-07-05
- Volume
- 13
- Issue
- 1
- Pages
- 3859-3859
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC9256719
- OpenAlex
- W4283816337
- Semantic Scholar
- f9a73a3b47c4de390aa82ac748aee83130ba7a72
- Cited by
- 132
- References
- 64
- Citations by year
- 2026: 19 · 2025: 43 · 2024: 36 · 2023: 26 · 2022: 8
- FWCI
- 8.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation Singapore (NRF-CRP15-2015-02)
- Agency for Science, Technology and Research (A18A5b0056)
- Ministry of Education - Singapore (A-0005138-00-00)
- National University of Singapore (A-0005947-06-00)
- Topics
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Plasmonic and Surface Plasmon Research
- Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Gold and Silver Nanoparticles Synthesis and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Keywords
- mid-infrared plasmonics, plasmonic nanoantennas, loss engineering, spectral multiplexing, vibrational spectroscopy, alcohol identification, microfluidic integration
- MeSH terms
- Machine Learning, Spectrophotometry, Infrared, Peptide Nucleic Acids, Surface Plasmon Resonance, Nanostructures
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
