Metal-Decorated InN Monolayer Senses N<sub>2</sub> against CO<sub>2</sub>
Bibliographic record
Abstract
Poor selectivity is a common problem faced by gas sensors. In particular, the contribution of each gas cannot be reasonably distributed when a binary mixture gas is co-adsorbed. In this paper, taking CO 2 and N 2 as an example, density functional theory is used to reveal the mechanism of selective adsorption of a transition metal (Fe, Co, Ni, and Cu)-decorated InN monolayer. The results show that Ni decoration can improve the conductivity of the InN monolayer while at the same time demonstrating an unexpected affinity for binding N 2 instead of CO 2 . Compared with the pristine InN monolayer, the adsorption energies of N 2 and CO 2 on the Ni-decorated InN are dramatically increased from −0.1 to −1.93 eV and from −0.2 to −0.66 eV, respectively. Interestingly, for the first time, the density of states demonstrates that the Ni-decorated InN monolayer achieves a single electrical response to N 2, eliminating the interference of CO 2 . Furthermore, the d-band center theory explains the advantage of Ni decorated in gas adsorption over Fe, Co, and Cu atoms. We also highlight the necessity of thermodynamic calculations in evaluating practical applications. Our theoretical results provide new insights and opportunities for exploring N 2 -sensitive materials with high selectivity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".