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Record W2728154772 · doi:10.14447/jnmes.v13i4.138

Electrochemical Characterization of InN Thin Films for Biosensing Applications

2010· article· en· W2728154772 on OpenAlexvenueno aff
Antonio M. Basilio, Yu‐Kuei Hsu, C. C. Chang, Peiping Wei, Abhijit Ganguly, H.C. Shih, Yit‐Tsong Chen, Li–Chyong Chen, Kuei‐Hsien Chen

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsnot available
Fundersnot available
KeywordsPhotocurrentPhotocathodeMaterials scienceThin filmElectrochemistryCathodic protectionElectrodeOptoelectronicsAnalytical Chemistry (journal)Characterization (materials science)BiasingNanotechnologyChemistryElectronVoltagePhysical chemistry

Abstract

fetched live from OpenAlex

InN, a well-established optoelectronic material, is currently being considered as a promising material for sensor applications. In this study, the fundamental electrochemical properties of InN thin film and its potential for electrode chemical and bio-sensing applications are demonstrated. The cyclic voltammograms of different concentrations of dopamine solution in 1 M HClO4 were measured. Similarly, potentiostatic measurements at 1 V versus Ag/AgCl show stable responses and linear change of current density with concentrations up to 0.475 mM dopamine. The InN thin film also demonstrated repeatable positive photoresponse to cathodic currents in 1 mM Ru(NH3)63+ in 1M KCl solution under a 100 mW cm-2 808-nm laser light illumination at a constant -0.25 V (vs. Ag/AgCl) bias. The cathodic current response showed a 27% enhancement, demonstrating its potential as photocathode. This cathodic photocurrent behavior is explained through the electron accumulation of the InN material.

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 score0.040
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.253
Teacher spread0.243 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

Explore more

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