3D Hierarchically Porous High‐Mass Loading SiO <sub>x</sub> Anodes Enabled by Consecutive Multi‐Layer Printing and Mid‐Infrared Laser Annealing
Bibliographic record
Abstract
Abstract Developing high‐energy‐density SiO x anodes for lithium‐ion batteries requires the strategy to address critical issues related to poor electron/ion transport kinetics and large volumetric changes during cycling. In this study, a novel approach is presented that combines digitally programmable 3D printing and mid‐infrared laser annealing techniques. The chemical scheme is designed for synthesizing carbon‐SiO x nanocomposites using a soft‐templated sol–gel method, in which molecularly incorporated carbon nanodomains are capable of efficiently absorbing mid‐infrared wavelength photons. During laser annealing, the carbon nanodomains serving as photothermal agents enable not only a highly efficient, localized carbothermal reduction to produce electrochemically active SiO x but also trigger the carbonization/graphitization of the polyacrylic acid binder for forming an electrically conductive framework. Consequently, this results in the formation of dual‐porous SiO x anode, featuring mesopores (≈8 nm in diameter) and macropores (100–600 nm in diameter). In parallel, the digitally programmable 3D printing process defines a grid‐pore channel architecture (with a spacing of ≈200 µm). It comprehensively enhances electron/ion transport and structural integrity in ultrathick electrodes. The resulting 3D anode achieves a high areal capacity of 9.5 mAh cm −2 at a mass loading as high as 6.6 mg cm −2 . Combinatorial analyses reveal that the 3D‐printed and laser‐annealed SiO x anode achieves a significantly enhanced electrochemical performance, attributed to a substantial increase in electrical conductivity and Li‐ion diffusion coefficient, along with the formation of a LiF‐rich thin SEI layer.
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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) | 0.000 | 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 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".