FlexibleâStretchable Micro Lithium Ion Batteries for Implantable, Wearable and Embedded Electronics
Bibliographic record
Abstract
Future generation electronics which will be in the form of implantable, wearable and/or embedded format will require reliable power supply. Remote wireless charging or grid power supplied battery or energy harvesting – in any form of power supply, energy storage or battery will play critical role. In that regard, coin cell batteries and/or super capacitors are not suitable due to their bulky profile and toxic materials involved in their manufacturing. From that perspective, rechargeable micro lithium ion batteries (mLIB) are promising option for such energy storage. They offer high operating voltage, long life and high energy capacity. However, one major challenge is to achieve flexible and stretchable battery which is critical physical form for implantable, wearable and/or embedded electronics. In recent years, two routes have been pursued for such flexible mLIB fabrication: (1) exploration of new types of inherently compliant materials for battery electrodes such as carbon nanotube, graphene, carbon grease and slurry mixes of nanomaterials and (2) thinned inorganic thin film based battery. Although nano-scale thin films based mLIBs are reliable from their manufacturing and performance perspective, they are fabricated on rigid substrates. One option is to use exfoliation of a mica substrate to release battery stack – however, mica substrates are unconventional and physical delamination of such substrate is a low throughput/low yield process. In this work we present a soft etch back based flexible mLIB formation process. First we fabricate the battery on bulk mono-crystalline silicon (100) using lithium based thin films including cobalt, phosphorous alloy and then we spin a soft material on top of it. Next we flip the whole system and etch back the silicon using reactive ion etching in sequential manner. Next we spin another soft material on the flipped “chip”. Finally, we remove the first soft material selectively over the thin films present and the second soft material. In this way, we obtain 40 mm thick silicon based mLIB with an enhanced normalized capacity of 146 mAh/cm2 even after 120 cycles of continued operation. Comparison with a traditional rigid battery (fabricated in batch with the flexible one) shows comparable to superior performance.
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.
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) | 0.003 | 0.002 |
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".