Dendritic Copper Current Collectors as a Capacity Boosting Material for Polymer-Templated Si/Ge/C Anodes in Li-Ion Batteries
Bibliographic record
Abstract
Dendritic copper offers a highly effective method for synthesizing porous copper anodes due to its intricate branching structure. This morphology results in an elevated surface area-to-volume ratio, facilitating shortened electron pathways during aqueous and electrolyte permeation. Here, we demonstrate a procedure for a time- and cost-efficient synthesis routine of fern-like copper microstructures as a host for polymer-templated Si/Ge/C thin films. Dissolvable Zintl clusters and sol–gel chemistry are used to synthesize nanoporous coating as the anode. Cyclic voltammetry (CV) with KOH as the electrolyte is used to estimate the surface area increase in the dendritic copper current collectors (CCs). Half cells are assembled and tested with battery-related techniques such as CV, galvanostatic cycling, and electrochemical impedance spectroscopy, showing a capacity increase in the dendritic copper cells. Energy-dispersive X-ray spectroscopy is used to estimate the removal of K in the bulk after oxidizing the Zintl phase K 12 Si 8 Ge 9 in the polymer/precursor blend with SiCl 4 . Furthermore, scanning electron microscopy images are provided to depict the thin films after synthesis and track the degradation of the half cells after cycling, revealing that the morphological degradation through alloying/dealloying is reduced for the dendritic Cu CC anodes as compared with the bare reference. Finally, we highlight this time- and cost-efficient routine for synthesizing this capacity-boosting material for low-mobility and high-capacity anode coatings.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".