Unlocking pseudocapacitors prolonged electrode fabrication via ultra-short laser pulses and machine learning
Bibliographic record
Abstract
Pseudocapacitors outperform lithium-ion batteries in terms of charging rate and power density. However, their electrode manufacturing procedures are prolonged and environmentally unfriendly, posing a research challenge. To address this issue, the one-step synthesis approach of ultra-short laser pulses for in situ nanostructure generation (ULPING) has been proposed. The generated nanostructures on the substrate through ULPING depend on the laser parameters, which leaves room for improvement in this field. The present study aims to build a theoretical bridge between the laser parameters used in the fabrication of pseudocapacitors and their electrochemical performance through machine learning approaches. Gaussian process regression (GPR), random forest (RF), and artificial neural network (ANN) have been employed to mimic the electrochemical behavior of pseudocapacitors, demonstrating the potential of ULPING in generating nanostructures on transition metals that can serve as pseudocapacitor electrodes. This research presents a promising method for producing binder-free and carbon-free pseudocapacitor electrodes efficiently and sustainably.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".