Mesoporous Nanoarchitectures for Electrochemical Energy Conversion and Storage
Bibliographic record
Abstract
Mesoporous materials have attracted considerable attention because of their distinctive properties, including high surface areas, large pore sizes, tunable pore structures, controllable chemical compositions, and abundant forms of composite materials. During the last decade, there has been increasing research interest in constructing advanced mesoporous nanomaterials possessing short and open channels with efficient mass diffusion capability and rich accessible active sites for electrochemical energy conversion and storage. Here, the synthesis, structures, and energy-related applications of mesoporous nanomaterials are the main focus. After a brief summary of synthetic methods of mesoporous nanostructures, the delicate design and construction of mesoporous nanomaterials are described in detail through precise tailoring of the particle sizes, pore sizes, and nanostructures. Afterward, their applications as electrode materials for lithium-ion batteries, supercapacitors, water-splitting electrolyzers, and fuel cells are discussed. Finally, the possible development directions and challenges of mesoporous nanomaterials for electrochemical energy conversion and storage are proposed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".