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 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 | 0.000 |
| 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 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".