Tuning g-C3N4 with Cr, Mo, and W for superior HCN gas detection: A first-principles study on adsorption, electronic structure, and sensing mechanism
Bibliographic record
Abstract
This study investigates the adsorption of hydrogen cyanide (HCN) molecules onto both pure and transition metal (TM)-modified graphitic carbon nitride (g-C3N4) using density functional theory (DFT) and first-principles calculations in the solid state. The findings demonstrate that HCN adsorption induces structural changes in both unmodified and TM-doped g-C3N4 surfaces. Specifically, the initially flat structures of these materials transform into curved configurations following interaction with HCN gas. This structural modification correlates with a significant enhancement in electrical conductivity and improvements in electronic properties. Analysis of the partial density of states for TM-doped g-C3N4 and the orbitals of adsorbed HCN molecules reveals that the presence of transition metals and HCN adsorption increase electron density near the Fermi level. The calculated adsorption energies (Eads) for HCN on various surfaces are as follows: -0.281 eV on pure g-C3N4, -2.213 eV on Cr-doped, -2.326 eV on Mo-doped, and -3.104 eV on W-doped g-C3N4. On the pristine surface, HCN exhibits weak physical interaction, whereas the bonding becomes considerably stronger with the introduction of transition metals. The adsorption energy values suggest that HCN is physically adsorbed on the pure g-C3N4 surface, as the value exceeds -1 eV. Conversely, on TM-modified surfaces, the adsorption indicates chemical bonding, with energy values less than -1 eV. Among the modified systems, W-doped g-C3N4 exhibits the strongest interaction with HCN, with the lowest Eads value of -3.104 eV, surpassing the Cr- and Mo-doped variants. This characteristic makes W-doped g-C3N4 a promising candidate for detecting and capturing HCN molecules from environmental sources.
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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".