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Record W4283641654 · doi:10.1149/1945-7111/ac7c3b

Review—Solid State Sensors for Phosphate Detection in Environmental and Medical Diagnostics

2022· article· en· W4283641654 on OpenAlexafffund
Vinay Patel, Peter Kruse, P. Ravi Selvaganapathy

Bibliographic record

VenueJournal of The Electrochemical Society · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcMaster University
FundersGlobal Water FuturesNatural Sciences and Engineering Research Council of Canada
KeywordsColorimetryAmperometryPotentiometric titrationSolid-stateReagentElectrochemistryElectrochemical gas sensorDetection limitProcess engineeringPhosphateElectrochemical cellNanotechnologyComputer scienceMaterials scienceChemistryElectrodeChromatographyEngineering

Abstract

fetched live from OpenAlex

Phosphorus is required for plants and humans to survive because it is needed for cell signaling, skeletal integrity, energy storage and metabolism. Phosphorus measurements are performed using colorimetric and electrochemical methods. Colorimetry is the most accepted method for commercial devices for phosphorus monitoring while electrochemical systems are still in the research phase. Here we provide the first comprehensive review of solid-state sensors for phosphate monitoring. The review focuses on solid state reagent storage for colorimetric sensors and different materials used in solid state electrochemical sensors. The electrochemical sensors are further classified into three groups: potentiometric, amperometric and voltammetric. All sensors are evaluated based on parameters such as measurement range, limit of detection (LOD), working pH and response time. Finally, we discuss limitations of the current sensors and future directions for the development of these sensors.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.036
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.220
Teacher spread0.215 · 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

Citations17
Published2022
Admission routes2
Has abstractyes

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