Well‐Defined Nanostructures for Electrochemical Energy Conversion and Storage
Bibliographic record
Abstract
Abstract Electrochemical energy conversion and storage play crucial roles in meeting the increasing demand for renewable, portable, and affordable power supplies for society. The rapid development of nanostructured materials provides an alternative route by virtue of their unique and promising effects emerging at nanoscale. In addition to finding advanced materials, structure design and engineering of electrodes improves the electrochemical performance and the resultant commercial competitivity. Regarding the structural engineering, controlling the geometrical parameters (i.e., size, shape, hetero‐architecture, and spatial arrangement) of nanostructures and thus forming well‐defined nanostructure (WDN) electrodes have been the central aspects of investigations and practical applications. This review discusses the fundamental aspects and concept of WDNs for energy conversion and storage, with a strong emphasis on illuminating the relationship between the structural characteristics and the resultant electrochemical superiorities. Key strategies for actualizing well‐defined features in nanostructures are summarized. Electrocatalysis and photoelectrocatalysis (for energy conversion) as well as metal‐ion batteries and supercapacitors (for energy storage) are selected to illustrate the superiorities of WDNs in electrochemical reactions and charge carrier transportation. Finally, conclusions and perspectives regarding future research, development, and applications of WDNs are discussed.
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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) | 0.000 | 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 | 0.001 |
| 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".