Gelatin–Organic Acid‐Based Biodegradable Batteries for Stretchable Electronics
Bibliographic record
Abstract
As the environmental pollution caused by electronic products becomes increasingly severe, the development and application of biodegradable batteries have become more important. Traditional biodegradable batteries are limited by low power output, low capacity, and lack of flexibility and stretchability, restricting their range of applications. Herein, a biodegradable battery made from magnesium–molybdenum electrodes and gelatin‐organic acid electrolytes such as lactic acid (LA)–gelatin (gel) and the citric acid (CA)–gelatin (gel) is proposed. The addition of organic acids to the gelatin increases the ionic conductivity of the electrolyte and promotes its reaction with the magnesium electrode, effectively enhancing battery performance. In the experimental results, it is shown that the LA–gel‐based electrolyte achieves a maximum conductivity of 2.37 × 10−3 S cm−1, while the CA–gel‐based electrolyte demonstrates a low activation energy of 11.04 kJ mol−1. The highest open‐circuit voltage recorded for the CA–gel‐based electrolyte with the Mg anode and Mo cathode is 1.92 V, and the related plateau voltage is around 1.3 V. The maximum power and maximum capacity achieved by the Mg‐based battery are 76.8 μW and 1.36 mAh cm−2, respectively, at 40 μA cm−2 for LA–gel battery. Moreover, the battery can be stretched to 80% strain while maintaining stable capacity.
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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) | 0.002 | 0.001 |
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".