High-Throughput Experimental Technology: Rapid Identification of the Precious Metal Modified In<sub>2</sub>O<sub>3</sub> for NO<sub>2</sub> Low-Temperature Sensing
Bibliographic record
Abstract
It often takes much time to obtain the components of high-performance metal oxide semiconductor gas-sensitive materials by inefficient “trial and error method.” In this study, high-throughput experimental technology (HTET) was used to modify the surface of Indium trioxide (In2O3) nanoparticles, and the In2O3 precious metal gas-sensitive sensors with different surface modification rates were prepared. The gas-sensitive properties of the sensors were systematically studied. HTET can accelerate the synthesis of materials and the screening of gas-sensitive properties, and it significantly improves experimental efficiency. The results show that the 0.5 mol% silver modified In2O3 (Ag$_{{0.5}}$In) sensor leads to an ultrahigh response (${R}_{\text {gas}}/{R}_{\text {air}} =923.6$) to 5 ppm NO2 at 50 °C 5.75 times of the pure In2O3 sensor. In addition, the sensor exhibits fast response (61.3 s) and recovery (106.3 s) times, high selectivity, and stable repeatability. The enhancement of the excellent NO2 gas sensitivity is reached mainly due to synergistic effect of the catalysis of the precious metals and the increase of surface chemisorption oxygen.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".