A review of emerging trends, challenges, and opportunities for utilization of metal nanoclusters in <scp>CO<sub>2</sub></scp> capturing
Bibliographic record
Abstract
Abstract CO2, a predominant anthropogenic greenhouse gas, emerges as a primary factor in climate change due to the increasing utilization of fossil fuels, necessitating immediate efforts for the development and implementation of strategies like carbon capture and storage (CCS) to mitigate emissions, considering the ongoing dependence on unsustainable energy and transportation resources. The research endeavours to meet the critical requirement for effective CO2 capture through the exploration of novel sorbent materials, with a specific focus on molecularly precise nanoclusters (NCs), aiming to enhance understanding of the catalytic mechanisms in CO2 reduction and design stable, high‐performance sorbents with controllable properties. Advancing the field, the study delves into the synthesis and examination of molecularly precise nanoclusters (NCs), an emerging domain in nanoscience, with a particular emphasis on well‐defined nanoclusters like thiolate‐protected Au, Ag, and Cu NCs. This strategy provides a distinctive foundation for attaining atomic‐level understanding of electrocatalytic CO2 reduction mechanisms, offering a more precise and customized synthesis to overcome challenges associated with polydispersity in conventional nanoparticles. The study highlights the exceptional catalytic activity of specific Au NCs like Au25 in converting CO2 to CO. It surpasses thermodynamic limits. The study also investigates the influence of surface properties, electrostatic, and steric stability on preventing nanocluster aggregation. It emphasizes the potential of molecularly precise nanoclusters as catalysts for CO2 reduction. Additionally, it suggests avenues for advanced sorbent development with improved performance and stability.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".