Bayesian-Optimization-Assisted Laser Reduction of Poly(acrylonitrile) for Electrochemical Applications
Bibliographic record
Abstract
Laser reduction of polymers has recently been explored to rapidly and inexpensively synthesize high-quality graphitic and carbonaceous materials. However, in past work, laser-induced graphene has been restricted to semiaromatic polymers and graphene oxide; in particular, poly(acrylonitrile) (PAN) is claimed to be a polymer that cannot be laser-reduced successfully to form electrochemically active material. In this work, three strategies to surmount this barrier are employed: (1) thermal stabilization of PAN to increase its sp 2 content for improved laser processability, (2) prelaser treatment microstructuring to reduce the effects of thermal stresses, and (3) Bayesian optimization to search the parameter space of laser processing to improve performance and discover morphologies. Based on these approaches, we successfully synthesize laser-reduced PAN with a low sheet resistance (6.5 Ω sq –1 ) in a single lasing step. The resulting materials are tested electrochemically, and their applicability as membrane electrodes for vanadium redox flow batteries is demonstrated. This work demonstrates electrodes that are processed in air, below 300 °C, which are cycled stably over 2 weeks at 40 mA cm –2, motivating further development of laser reduction of porous polymers for membrane electrode applications such as RFBs.
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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 | 0.000 | 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 | 0.000 |
| 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".