3D-printed topological-structured electrodes with exceptional mechanical properties for high-performance flexible Li-ion batteries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A vital aspect in advancing flexible batteries is the development of flexible electrodes capable of enduring repeated stretching while upholding satisfactory electrochemical performance. Thus, adopting a systematic and efficient approach to structural design and fabrication becomes imperative. In this study, we introduce an optimal structural design achieved through topology optimization and fabricate flexible electrodes via 3D printing, representing a departure from traditional design and manufacture methodologies in the development of flexible electrodes for batteries. Our research underscores the impressive mechanical strength of these topologically-structured electrodes (TSEs), validated through rigorous finite element analysis (FEA) and tensile strength testing. The results of both the stretch deformation and twist deformation analysis on the TSEs and the conventional mesh-structured electrodes (MSEs) show that the peak strain and stress of TSEs are much lower than those of MSEs. Notably, even under 50 % stretching, the TSEs maintain structural integrity, contrasting sharply with conventional mesh-structured electrodes (MSEs) and flat film electrodes which often crack under similar conditions. Moreover, after enduring 50 cycles of stretching, the TSEs retain an outstanding 98 % of their original capacity, surpassing MSEs which retain only 80 % of their capacity. These findings highlight the significant potential of topologically designed flexible electrodes, offering promising avenues for the development of stretchable and flexible energy storage devices such as wearable tech and bio-integrated electronics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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 it