The DOI matches its Crossref record.
Rapid analysis of organic carbon and nitrogen in particulate materials
Open accessAbstract
Abstract A method for rapid determination of organic carbon and nitrogen in natural particulate materials is described. The procedure allows the determination of 60 samples per day by a single analyst. It involves the determination of carbon and nitrogen using an automatic CN-analyzer following the partitioning of inorganic and organic carbon phase by acidification with 25% HCl in situ within silver sample cups. The technique eliminates reweighing procedures, losses of organic and inorganic compounds (except carbonates and sulphides) and is free from matrix interferences. Extensive testing and application showed long-term precisions for organic carbon and nitrogen of about 3%. The accuracy is excellent, irrespective of the calcium carbonate content of the sample.
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Cited by
Cocheret de la Morinière, E., Pollux, B., Nagelkerken, I., Hemminga, M., Huiskes, A., & van der Velde, G. (2003) · Marine Ecology Progress Series · doi:10.3354/meps246279 · Cited by 307
Metadata
- Authors
- Nieuwenhuize, J., Maas, Y. E. M., & Middelburg, J. J.
- Venue
- Marine Chemistry
- Publisher
- Elsevier BV
- Published
- 1994-02
- Volume
- 45
- Issue
- 3
- Pages
- 217-224
- ISSN
- 0304-4203 (ISSN-L)
- OpenAlex
- W2014785930
- Cited by
- 564
- References
- 0
- FWCI
- 11.32
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Rapid analysis of organic carbon and nitrogen in particulate materials. https://app.cochat.ai/papers/10.1016/0304-4203(94)90005-1
- The paper
- Nieuwenhuize, J., Maas, Y. E. M., & Middelburg, J. J. (1994). Rapid analysis of organic carbon and nitrogen in particulate materials. Marine Chemistry. https://doi.org/10.1016/0304-4203(94)90005-1
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
