One-Step Synthesis of Si-Graphene Heterostructures via In-Flight Gas-Phase Mixing for High-Capacity Silicon-Rich Anodes
Bibliographic record
Abstract
The deliberate assembly of heterostructures has emerged as a powerful strategy for electrochemical energy storage, where integration of complementary components enables synergistic performance gains. Moving beyond serial production of individual components and their subsequent liquid phase assembly, we report a one-step, continuous gas-phase synthesis of high-purity silicon/few-layer graphene (Si–FLG) heterostructures by coupling microwave-plasma and hot-wall reactors. This in-flight assembly yields exceptionally pure amorphous Si uniformly integrated within conductive FLG, eliminating liquid-phase processing. Electrochemical performance exhibits a non-monotonic dependence of performance on FLG content: capacity and cycle life maximize at an intermediate 15 wt.% FLG, attributed to a percolated, strain-buffering FLG network that preserves electrical contact while minimizing inactive mass. The optimized heterostructure delivers specific capacities of ~ 2800 mAh g -1 Si + FLG (0.05 C) and ~ 1400 mAh g -1 Si + FLG after 100 cycles at 1 C, outperforming other Si/graphene systems reported in the literature under similar conditions. These results highlight gas-phase self-assembly as a scalable route to integrate 0D/2D nanostructures into high-capacity, long-life Li-ion anodes and establish a new performance benchmark for low-carbon-fraction Si/graphene composites.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".