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Record W4226258015 · doi:10.1109/jsen.2022.3163476

Improving the Performance of All-Solid-Stated Planar pH Sensor With Heat Treated Process

2022· article· en· W4226258015 on OpenAlexaff
Kun Xu, Cheng Chen, Yunqing Tang, Xiliang Zhang, Caizhang Wu, Miaomiao Geng, Lijun Sun

Bibliographic record

VenueIEEE Sensors Journal · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Alberta
FundersHenan University of Technology
KeywordsElectrodeMaterials scienceWorking electrodeAnalytical Chemistry (journal)Reference electrodeElectrochemical gas sensorPotentiometric sensorElectrochemistrySensitivity (control systems)OxideInorganic chemistryChemical engineeringOptoelectronicsMetallurgyChemistryElectronic engineeringPotentiometric titration

Abstract

fetched live from OpenAlex

All-solid-stated pH sensors based on metal/ metal oxide were widely used in mixed heterogeneous environment, such as soil or soilless cultivation. In this study, an all-solid-stated pH sensor prepared by magnetron sputtering was presented, of which the working electrode was antimony, and reference electrode was Ag/AgCl electrode, and heat treated process was conducted to improving the performance of all-solid-stated planar pH sensor. The electrodes heat treated at the temperature of 100°C, 200°C, 300°C, and 400°C were tested through surface morphology analysis, energy spectrum analysis and electrochemical experiments. The results showed that the heat-treated temperature had a remarkable effect on the performance of the pH sensor. Specifically, the pH sensor heat-treated at 200°C had excellent performance, with the sensitivity 74.0 mV/pH, the response time less than 3 s, the standard deviation less than 2 mV in 700 s period. The developed sensor can be applied in pH detection in soilless culture and is promising to popularize in industrial production.

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)0.000
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 score0.045
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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