Highly Aligned Graphene Oxide for Lithium Storage in Lithium‐Ion Battery Through A Novel Microfluidic Process: The Pulse Freezing
Bibliographic record
Abstract
Abstract The modification of carbon‐based lithium‐ion batteries electrodes is required for the growing need of more reliable electric vehicles. Herein, a new method is introduced to fabricate vertically aligned graphene oxide (GO) films as free‐standing carbon lithium hosts for lithium‐ion batteries with enhanced performance. Vertical alignments are induced of GO in a microfluidic channel by controlling flow rates and patterns of GO suspensions. The vertical alignments are preserved and spontaneously form porous microstructures by Pulse Freezing the GO solutions inside the channel. This combined process results in the increase of levels of microstructure porosity and vertical alignment of GO films. The alignment and porous microstructures increase both electron and ion transfer capabilities across the prepared film. The half‐cell performance of aligned GO films shows a specific capacity of 440 mAh g−1 at a current density of 0.5 A g−1 after 150 cycles. This is a 190% specific capacity increase compared to the performance of a half‐cell prepared with GO without the high level of vertical alignment and microporosity. The significant increase in the value and stability of specific capacity and higher rates of charge transfer favor the promising application of carbon‐based lithium‐ion batteries for electric transportation industries.
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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 | 0.000 |
| 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 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".