Triblock Polymer/Acetylene Black Dual‐Assisted Preparation of Ge/C Nanocomposites with Superior Lithium Storage Performance
Bibliographic record
Abstract
Abstract Anode materials based on IV main group elements like Si, Ge, and Sn show great potential for lithium‐ion batteries (LIBs) due to their high specific capacity and low working potential. However, issues such as volume expansion and lattice pulverization hinder their practical usage. To address these issues for Ge, a novel F127 triblock polymer/acetylene black dual‐assisted strategy is proposed to achieve uniform dispersion of polycrystalline Ge, enabling the preparation of Ge@C nanocomposites via hydrogen reduction. The introduced F127 triblock polymer and acetylene black serves a dual purpose to enhance electrical conductivity and prevent Ge nanoparticles from agglomeration. When tested as anode material for LIBs, the Ge@C nanocomposites exhibit exceptional electrochemical performances, demonstrating a sustained specific discharge capacity of 780 mA h g −1 at 0.2 A g −1 after 100 cycles. Moreover, the capacity remains at 767 mA h g −1 even after 300 cycles at a higher current density of 0.5 A g −1 . These enhanced lithium storage performances are attributed to the combined effects of well‐dispersed tiny Ge nanoparticles, uniform carbon coating, and an abundance of defects. These factors effectively mitigate the volume expansion and lattice pulverization of Ge nanoparticles and concurrently enhance their conductivity, leading to improved overall performance in LIBs.
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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 | 0.000 |
| 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) | 0.001 | 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 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".