MXenes as Effective Sulfur Hosts and Electrocatalysts to Suppress Lithium Polysulfide Shuttling: A Computational Study
Bibliographic record
Abstract
Exploring electrocatalysts for the sulfur reduction reaction (SRR) has emerged as a promising strategy to suppress the shuttle effect and enhance the kinetics in lithium–sulfur (Li–S) batteries. A comprehensive understanding of the electrocatalytic mechanism within Li–S batteries remains elusive, which hinders the rational design of advanced electrocatalysts for these systems. In this study, two-dimensional (2D) transition metal carbides and nitrides (MXenes) have been investigated for the catalytic conversion of lithium polysulfides (LiPSs) using density functional theory (DFT). Our findings reveal that MXenes show a moderate binding affinity for LiPSs, suggesting favorable thermodynamics for their role as electrocatalysts for the SRR. This thermodynamic favorability promotes the suppression of the LiPSs’ shuttle effect and the enhancement of the SRR kinetics. The SRR process in Li–S batteries consists of multiple steps with varying activation energies. Our analysis by constructing the energy diagram for the multistep SRR indicates that the initial reduction of S 8 to Li 2 S 8 is facile with a lower activation energy, while the last step where Li 2 S 2 converts to Li 2 S appears to be a rate-limiting step. To predict the catalytic abilities of MXene structures, we built a volcano-shaped relationship between the adsorption of LiPSs and catalytic activity. We show three MXenes─Ta 2 CO 2, Zr 2 NO 2, and Mo 2 NO 2 as potential electrocatalysts that exhibit lower thermodynamic overpotentials for the SRR. These findings represent a significant step toward developing advanced electrocatalysts that may unlock the full potential of Li–S batteries, paving the way for improved energy storage systems with enhanced efficiency and performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".