Optimizing the Ultrashort Laser Pulses for In Situ Nanostructure Generation Technique for High-Performance Supercapacitor Electrodes Using Artificial Neural Networks and Simulated Annealing Algorithms
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Transition metals (TMs) are being investigated as electrodes for pseudocapacitors, where an oxide layer is necessary to allow for rapid redox reactions. In this work, we utilized an in situ, rapid, binder-free, and green method for the fast fabrication of pseudocapacitor electrodes called ultrashort laser pulses for in situ nanostructure generation (ULPING) to form oxide layers on a titanium sheet. By utilizing this fabrication technique on a titanium sheet, a specific areal capacitance of 0.3579 mF cm –2 was achieved at a current density of 0.25 mA cm –2 . However, the laser fabrication parameters were selected experimentally and resulted in low performance of pseudocapacitors. Therefore, one of the main objectives of this study was to find the optimal laser fabrication parameters to achieve the highest specific areal capacitance. A large dataset was generated to find the relationship between the laser fabrication parameters and the electrochemical behavior performance (impedance and specific areal capacitance) of the fabricated electrodes by using an artificial neural network (ANN). We used an optimization algorithm (simulated annealing-SA) to overlook the trained ANN model as a black box and try to maximize the objective function, which in our case is a specific capacitance value, to find the most optimal laser fabrication parameters. Using SA, optimal laser fabrication parameters were found, which increased the specific areal capacitance to 0.9999 mF cm –2 at a current density of 0.25 mA cm –2 . The results demonstrated that the conducted study has the potential to introduce effective techniques for utilizing ULPING to produce nanoscale structures on TMs. These structures have the potential to be employed as electrodes in pseudocapacitors. Additionally, the research underscores the significance of employing data-driven approaches in electrode design procedures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".