Editorial: Micro/nano materials for energy storage and conversion
Bibliographic record
Abstract
Editorial on the Research Topic Micro/nano materials for energy storage and conversionThe demand for energy is increasing dramatically at an alarming rate, resulting from rapid economic development and the ever-increasing requirements of energy-based appliances.With energy usage increasing, concerns about environmental Research Topic and the social problems associated with the consumption of conventional fossil fuels are becoming serious (Yu D. et al., 2014;Fang R. et al., 2017).As a cutting-edge approach, nanotechnology has opened new frontiers in the field of materials science and engineering to meet the challenge by designing novel materials, especially micronanometer, subnano, and even atomic scale materials, for efficient energy storage and conversion.Recently, the applications of micro/nano materials in energy storage and conversion fields, including lithium batteries, metal-ion batteries, water splitting, photocatalytic reactions, and electrochemical catalysis, have been widely investigated (Dai L. et al., 2015;Hao J. et al., 2020;Zhang S. et al., 2022).However, the practical application of micro/nano materials is still far from being satisfactory, as it is mainly impeded by costs and efficiency.Therefore, the design of cost-saving and highly efficient micro/nano materials in the field of energy storage and conversion is still very significant.Numerous papers have been reported in this Research Topic, and herein we introduce the representative advances in the collected papers that discuss how micro/nano materials work in the area of energy conversion and storage.Currently, the highest energy density of lithium-ion batteries (LIBs) is approaching its limitation but is still unable to satisfy the growing requirements of electric vehicles.Furthermore, the high cost and safety Research Topic severely limit their large-scale practical application for renewable energy storage systems.It is urgent to develop an innovative and highly efficient battery system to meet the ever-increasing demands for energy.Cao et al. reported one electrospinning approach to synthesize a novel nanocompound, namely, the FeS 2 nanoparticles encapsulated in S/N co-doped threedimensional multi-channel structural carbon nanofibers (FeS 2 @ CNFs).The FeS 2 @ CNFs electrode exhibited an excellent rate property and cyclic stability as cathode materials for LIBs.The electrode also showed a high initial capacity of 1,336.7 mAh g -1 ,
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.036 | 0.028 |
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".