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Record W2995207999 · doi:10.1039/c9em00508k

A novel Mg(OH)<sub>2</sub> binding layer-based DGT technique for measuring phosphorus in water and sediment

2019· article· en· W2995207999 on OpenAlexaff
Fazhi Xie, Lu Li, Xiaoyan Sun, Tingting Hu, Kang Song, John P. Giesy, Qilin Wang

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsSaskatoon Medical ImagingUniversity of Saskatchewan
FundersNatural Science Foundation of Anhui ProvinceAustralian Research CouncilChinese Research Academy of Environmental SciencesNational Natural Science Foundation of China
KeywordsSedimentPhosphorusEnvironmental chemistryEnvironmental scienceLayer (electronics)ChemistryGeologyGeomorphology

Abstract

fetched live from OpenAlex

Diffusive gradients in thin films (DGT) have gained wide attention for in situ measurement of reactive phosphorus species (PO4) in natural water, sediments and potentially soils. In this study, a novel Mg(OH)2 binding gel was formed using magnesium hydroxide obtained by in situ hydration of calcined magnesium oxide. Laboratory scale experiments showed that the novel Mg(OH)2 gel had a homogeneous dispersion of fine particles of Mg(OH)2 with a particle size of 2-5 μm. With 10 mL of 2.0 mol L-1 NaOH as the eluting agent, the optimal elution efficiency of PO4 on the Mg(OH)2 gel was 72 ± 5%. There were linear relationships between the accumulated PO4 mass and the applied PO4 concentration (0.1 to 20 mg P per L), time (0 to 24 h) and temperature (22 to 40 °C). The capacity of the Mg(OH)2 binding layer was determined to be 99.5 μg P per disc. Tests in synthetic seawater, Chaohu Lake and Yihai Pond confirmed that Mg(OH)2-DGT was able to accurately measure phosphorus up to 10 days. This was indicated by the good agreements between the concentrations measured by DGT (CDGT) technology and by an ex situ chemical method in solution (Csoln), with a CDGT/Csoln ratio between 0.91 and 1.09.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.609

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.192
Teacher spread0.181 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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