Nanoscale assembling of graphene oxide with electrophoretic deposition leads to superior percolation network in Li-ion electrodes: TiNb<sub>2</sub>O<sub>7</sub>/rGO composite anodes
Bibliographic record
Abstract
In this paper, electrophoretic deposition (EPD) is shown to promote nanoscale assembling of graphene oxide (GO) enabling the fabrication of highly homogeneous, robust, and capacity fade resistant composite titanium niobate (TiNb2O7, TNO)/rGO anodes upon reductive annealing. Control tests revealed that EPD is superior to conventional PVDF-based casting in maximizing the performance benefits from using reduced GO in Li-ion electrode fabrication as is the case of TNO that is plagued with conductivity and capacity fading problems. In this particular study, we show that there is a synergy developed between GO and EPD with the former (1) stabilizing the EPD suspension, (2) acting as a flexible binder net that affords mechanical integrity during the volume expansion of TNO, (3) serving as a conductive filler, and (4) contributing to Li-ion storage via pseudocapacitance. As a consequence, a superior percolation network is developed. Thus while both EPD- and PVDF- built TNO/rGO composite anodes exhibited high initial capacities (∼350 and 318 mA h g-1) at 0.5 C cycling, respectively, their cycling behaviour was quite different with the latter experiencing high internal polarization and extended degradation. Post-mortem PEEM-XANES analysis clearly demonstrated a highly homogeneous mesostructure in the case of the EPD-built TNO/rGO anode vs. a highly segregated and dis-jointed rGO and TNO component clustering in the PVDF-built electrode.
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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".