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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".