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Record W4407842013 · doi:10.1021/acsanm.4c07207

Hydrogen Sulfide Adsorption Regulation on Indium Oxide Nanosheets via Defect Engineering for Meat Spoilage Detection

2025· article· en· W4407842013 on OpenAlexaff
Zhan Wang, Kaibin Chu, Chenxi Guo, Yifei Wang, Xinliang Jiao, Jintao Xu, Bin Yan, Yunlong Xi, Peng Liu, Ning Han, Mingming Hua, Peng Zhang, Chunxiao Lv

Bibliographic record

VenueACS Applied Nano Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Shandong ProvinceLinyi UniversityNational Natural Science Foundation of China
KeywordsIndiumHydrogen sulfideAdsorptionFood spoilageOxideSulfideHydrogen sulfide sensorMaterials scienceChemistryChemical engineeringInorganic chemistryNanotechnologyMetallurgyBacteriaBiologyOrganic chemistrySulfurEngineering

Abstract

fetched live from OpenAlex

The limited sensitivity of indium oxide (In 2 O 3 ) gas-sensing materials for detection of hydrogen sulfide (H 2 S) restricts their application in identifying meat spoilage. In this study, two-dimensional indium oxide nanosheets (In 2 O 3 -NS) with intrinsic defects were synthesized using an “egg-box” structure, which was formed by the interaction between sodium alginate and metal cations, combined with an ice-templating method and annealing process. The resulting In 2 O 3 -NS sensor demonstrated a response of 950 to 5 ppm of H 2 S at its optimal operating temperature of 175 °C, with a detection limit as low as 100 ppb. Structural characterization and density functional theory calculations revealed that intrinsic defects in the In 2 O 3 -NS structure optimize the electron density distribution, providing additional adsorption sites for H 2 S and significantly enhancing gas sensitivity. Furthermore, a system utilizing In 2 O 3 -NS sensors was developed to evaluate meat freshness by detecting H 2 S emissions.

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 categoriesMeta-epidemiology (narrow)
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 score0.022
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.005
GPT teacher head0.186
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.

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

Citations7
Published2025
Admission routes1
Has abstractyes

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