First-Principles Study on Nb<sub>2</sub>C–X (X = S, Cl, F)/Graphene Heterostructures: Assessing Aqueous Stability and Implications for Electrocatalysis
Bibliographic record
Abstract
Nb 2 C–X MXenes (X = S, Cl, F) have the potential to be promising economical (electro)catalysts, but their degradation in oxidative and aqueous environments remains a major concern. In this work, the resistance to oxidation and hydrolysis of heterostructures made of Nb 2 C–X MXenes and graphene was explored using density functional theory. We found that Nb 2 C–X/graphene heterostructures are less prone to oxidation compared to pristine Nb 2 C–X MXene. Especially, Nb 2 C–F/graphene was found to possess higher oxidative resistance compared to those of Nb 2 C–S/graphene and Nb 2 C–Cl/graphene. An analysis of the electronic properties of the Nb 2 C–X/graphene heterostructures indicated improved conductivity compared to that of pristine Nb 2 C–X structures and gave insight into the influence of graphene on the MXene’s electronic structure. In addition, the resistance to hydrolysis of pristine Nb 2 C–S and the Nb 2 C–S/graphene heterostructures was compared. Nudged elastic band calculations indicated significantly higher activation energies for water adsorption and dissociation on the Nb 2 C–S/graphene heterostructure as compared to that on pristine Nb 2 C–S, which are the first steps in MXene decomposition in aqueous media. Moreover, an assessment of the oxygen evolution reaction (OER) performance showed a significantly lower overpotential for the OER on Nb 2 C–S/graphene compared to that of pristine Nb 2 C–S, indicating the improved electrocatalytic activity of the heterostructure. This work presents the critical role of graphene in improving the resistance to oxidation and stability in aqueous media of MXenes, which is a valuable insight for the synthesis of stable MXene-based (electro)catalysts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".