Chemically coupling SnO <sub>2</sub> quantum dots and MXene for efficient CO <sub>2</sub> electroreduction to formate and Zn–CO <sub>2</sub> battery
Bibliographic record
Abstract
Electrochemical conversion of CO 2 into formate is a promising strategy for mitigating the energy and environmental crisis, but simultaneously achieving high selectivity and activity of electrocatalysts remains challenging. Here, we report low-dimensional SnO 2 quantum dots chemically coupled with ultrathin Ti 3 C 2 T x MXene nanosheets (SnO 2 /MXene) that boost the CO 2 conversion. The coupling structure is well visualized and verified by high-resolution electron tomography together with nanoscale scanning transmission X-ray microscopy and ptychography imaging. The catalyst achieves a large partial current density of −57.8 mA cm −2 and high Faradaic efficiency of 94% for formate formation. Additionally, the SnO 2 /MXene cathode shows excellent Zn–CO 2 battery performance, with a maximum power density of 4.28 mW cm −2 , an open-circuit voltage of 0.83 V, and superior rechargeability of 60 h. In situ X-ray absorption spectroscopy analysis and first-principles calculations reveal that this remarkable performance is attributed to the unique and stable structure of the SnO 2 /MXene, which can significantly reduce the reaction energy of CO 2 hydrogenation to formate by increasing the surface coverage of adsorbed hydrogen.
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) | 0.000 | 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".