CoChat
Sign Up
Cited by 104

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions

Karbassiyazdi, Elika, Fattahi, Fatemeh, Yousefi, Negin, Tahmassebi, Amirhessam, Taromi, Arsia Afshar, Manzari, Javad Zyaie, Gandomi, Amir H, Altaee, Ali, Razmjou, Amir·2022·Environmental Research
DOI 10.1016/j.envres.2022.114286
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.envres.2022.114286

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 3 references

Research works printed but not linked: 44

  1. Bolan, 2021, Remediation of poly- and perfluoroalkyl substances (PFAS) contaminated soils – to mobilize or to immobilize or to degrade?, J. Hazard Mater., 401, 10.1016/j.jhazmat.2020.123892 · DOI · Paper page
  2. Chen, 2015, Xgboost: extreme gradient boosting, R package version 0.4-2, 1, 1
  3. Chen, 2017, Sorption of perfluorooctane sulfonate and perfluorooctanoate on polyacrylonitrile fiber-derived activated carbon fibers: in comparison with activated carbon, RSC Adv., 7, 927, 10.1039/C6RA25230C · DOI
  4. Cheng, 2019, Using machine learning to classify bioactivity for 3486 per- and polyfluoroalkyl substances (PFASs) from the OECD list, Environ. Sci. Technol., 53, 13970, 10.1021/acs.est.9b04833 · DOI
  5. Deng, 2012, Highly efficient sorption of perfluorooctane sulfonate and perfluorooctanoate on a quaternized cotton prepared by atom transfer radical polymerization, Chem. Eng. J., 193–194, 154, 10.1016/j.cej.2012.04.005 · DOI
  6. Deng, 2015, Enhanced adsorption of perfluorooctane sulfonate and perfluorooctanoate by bamboo-derived granular activated carbon, J. Hazard Mater., 282, 150, 10.1016/j.jhazmat.2014.03.045 · DOI
  7. Dixit, 2019, PFOA and PFOS removal by ion exchange for water reuse and drinking applications: role of organic matter characteristics, Environ. Sci. J. Integr. Environ. Res.: Water Research & Technology, 5, 1782
  8. Dolatabadi, 2019, A rapid and efficient removal approach for degradation of metformin in pharmaceutical wastewater using electro-Fenton process; optimization by response surface methodology, Water Sci. Technol., 80, 685, 10.2166/wst.2019.312 · DOI
  9. Dolatabadi, 2020, Mineralization of mefenamic acid from hospital wastewater using electro-Fenton degradation: optimization and identification of removal mechanism issues, Environ. Prog. Sustain. Energy, 39, 10.1002/ep.13380 · DOI
  10. Dolatabadi, 2021, A green approach to remove acetamiprid insecticide using pistachio shell-based modified activated carbon; economical groundwater treatment, J. Clean. Prod., 316, 10.1016/j.jclepro.2021.128226 · DOI
  11. Dolatabadi, 2021, Electro-Fenton approach in oxidative degradation of dimethyl phthalate - the treatment of aqueous leachate from landfills, Sci. Total Environ., 772, 10.1016/j.scitotenv.2021.145323 · DOI
  12. Domingo, 2019, Human exposure to per- and polyfluoroalkyl substances (PFAS) through drinking water: a review of the recent scientific literature, Environ. Res., 177, 10.1016/j.envres.2019.108648 · DOI · Paper page
  13. Du, 2015, Removal of perfluorinated carboxylates from washing wastewater of perfluorooctanesulfonyl fluoride using activated carbons and resins, J. Hazard Mater., 286, 136, 10.1016/j.jhazmat.2014.12.037 · DOI
  14. Fagbayigbo, 2017, Removal of PFOA and PFOS from aqueous solutions using activated carbon produced from Vitis vinifera leaf litter, Environ. Sci. Pollut. Control Ser., 24, 13107, 10.1007/s11356-017-8912-x · DOI
  15. Gao, 2017, Adsorptive removal of emerging polyfluoroalky substances F-53B and PFOS by anion-exchange resin: a comparative study, J. Hazard Mater., 323, 550, 10.1016/j.jhazmat.2016.04.069 · DOI
  16. Hassan, 2020, vol. 19
  17. Holmquist, 2020, An (Eco)Toxicity life cycle impact assessment framework for per- and polyfluoroalkyl substances, Environ. Sci. Technol., 54, 6224, 10.1021/acs.est.9b07774 · DOI
  18. Jeong, 2021, Predicting micropollutant removal by reverse osmosis and nanofiltration membranes: is machine learning viable?, Environ. Sci. Technol., 55, 11348, 10.1021/acs.est.1c04041 · DOI
  19. Kibbey, 2020, Supervised machine learning for source allocation of per- and polyfluoroalkyl substances (PFAS) in environmental samples, Chemosphere, 252, 10.1016/j.chemosphere.2020.126593 · DOI
  20. Kibbey, 2021, Source allocation of per- and polyfluoroalkyl substances (PFAS) with supervised machine learning: classification performance and the role of feature selection in an expanded dataset, Chemosphere, 275, 10.1016/j.chemosphere.2021.130124 · DOI
  21. Leo Kumar, 2017, State of the art-intense review on artificial intelligence systems application in process planning and manufacturing, Eng. Appl. Artif. Intell., 65, 294, 10.1016/j.engappai.2017.08.005 · DOI · Paper page
  22. Li, 2021, Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: a review, Chem. Eng. J., 405, 10.1016/j.cej.2020.126673 · DOI
  23. Liu, 2015, Understanding the adsorption of PFOA on MIL-101(Cr)-Based anionic-exchange metal–organic frameworks: comparing DFT calculations with aqueous sorption experiments, Environ. Sci. Technol., 49, 8657, 10.1021/acs.est.5b00802 · DOI
  24. Liu, 2020, Removal of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) from water by carbonaceous nanomaterials: a review, Crit. Rev. Environ. Sci. Technol., 50, 2379, 10.1080/10643389.2019.1700751 · DOI
  25. Meng, 2019, Efficient removal of perfluorinated compounds from water using a regenerable magnetic activated carbon, Chemosphere, 224, 187, 10.1016/j.chemosphere.2019.02.132 · DOI
  26. Merayo, 2019, Comparative analysis of artificial intelligence techniques for material selection applied to manufacturing in Industry 4.0, Procedia Manuf., 41, 42, 10.1016/j.promfg.2019.07.027 · DOI
  27. Pan, 2008, Efficient removal of aromatic sulfonates from wastewater by a recyclable polymer: 2-naphthalene sulfonate as a representative pollutant, Environ. Sci. Technol., 42, 7411, 10.1021/es801370n · DOI
  28. Pan, 2009, Effect of cationic and anionic surfactants on the sorption and desorption of perfluorooctane sulfonate (PFOS) on natural sediments, Environ. Pollut., 157, 325, 10.1016/j.envpol.2008.06.035 · DOI · Paper page
  29. Park, 2020, Magnetic ion-exchange (MIEX) resin for perfluorinated alkylsubstance (PFAS) removal in groundwater: roles of atomic charges for adsorption, Water Res., 181, 10.1016/j.watres.2020.115897 · DOI
  30. Pramanik, 2015, A comparative study of coagulation, granular- and powdered-activated carbon for the removal of perfluorooctane sulfonate and perfluorooctanoate in drinking water treatment, Environ. Technol., 36, 2610, 10.1080/09593330.2015.1040079 · DOI
  31. Qian, 2017, Perfluorooctane sulfonate adsorption on powder activated carbon: effect of phosphate (P) competition, pH, and temperature, Chemosphere, 182, 215, 10.1016/j.chemosphere.2017.05.033 · DOI
  32. Qu, 2009, Equilibrium and kinetics study on the adsorption of perfluorooctanoic acid from aqueous solution onto powdered activated carbon, J. Hazard Mater., 169, 146, 10.1016/j.jhazmat.2009.03.063 · DOI
  33. Radfar, 2021, The role of 3D printing in the fight against COVID-19 outbreak, J. 3d Print. Med., 10.2217/3dp-2020-0028 · DOI
  34. Raza, 2019, A machine learning approach for predicting defluorination of per- and polyfluoroalkyl substances (PFAS) for their efficient treatment and removal, Environ. Sci. Technol. Lett., 6, 624, 10.1021/acs.estlett.9b00476 · DOI
  35. Rusdah, 2020, XGBoost in handling missing values for life insurance risk prediction, SN Appl. Sci., 2, 1, 10.1007/s42452-020-3128-y · DOI
  36. Schepelina, 2008, Poly(2-(dimethylamino)ethyl methacrylate)-modified nanoporous colloidal films with pH and ion response, Langmuir, 24, 14188, 10.1021/la802453z · DOI
  37. Singh, 2012, Artificial intelligence based modeling for predicting the disinfection by-products in water, Chemometr. Intell. Lab. Syst., 114, 122, 10.1016/j.chemolab.2012.03.014 · DOI
  38. Steigerwald, 2021, Adsorption behavior of perfluorooctanesulfonate (PFOS) onto activated spent coffee grounds biochar in synthetic wastewater effluent, J. Hazard. Mater. Lett., 2
  39. Su, 2021, A database framework for rapid screening of structure-function relationships in PFAS chemistry, Sci. Data, 8, 14, 10.1038/s41597-021-00798-x · DOI
  40. Tahmassebi, 2022, An explainable prediction framework for engineering problems: case studies in reinforced concrete members modeling, Eng. Comput., 39, 609, 10.1108/EC-02-2021-0096 · DOI
  41. Uraikul, 2007, Artificial intelligence for monitoring and supervisory control of process systems, Eng. Appl. Artif. Intell., 20, 115, 10.1016/j.engappai.2006.07.002 · DOI
  42. Wang, 2019, Adsorption behavior and mechanism of emerging perfluoro-2-propoxypropanoic acid (GenX) on activated carbons and resins, Chem. Eng. J., 364, 132, 10.1016/j.cej.2019.01.153 · DOI
  43. Zhang, 2021, Sorption of perfluoroalkylated substances (PFASs) onto granular activated carbon and biochar, Environ. Technol., 42, 1798, 10.1080/09593330.2019.1680744 · DOI · Paper page
  44. Zhao, 2011, Sorption of perfluorochemicals to granular activated carbon in the presence of ultrasound, J. Phys. Chem., 115, 2250, 10.1021/jp111784k · DOI

Cited by

Sorted by citations

See all 104

Metadata

Authors
Karbassiyazdi, E., Fattahi, F., Yousefi, N., Tahmassebi, A., Taromi, A. A., Manzari, J. Z., Gandomi, A. H., Altaee, A., & Razmjou, A.
ORCID
Venue
Environmental Research
Publisher
Elsevier BV
Published
2022-12
Volume
215
Pages
114286
ISSN
0013-9351
DOI
10.1016/j.envres.2022.114286
Cited by
104
References
3
Language
en
Licence
Funders
University of Technology Sydney

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.

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