Improved synthesis of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXenes resulting in exceptional electrical conductivity, high synthesis yield, and enhanced capacitance
Bibliographic record
Abstract
For the first time, an "Evaporated-Nitrogen" Minimally Intensive Layer Delamination (EN-MILD) synthesis approach is reported to synthesize exceptionally high quality MXene sheets. In the EN-MILD method, the concentrations of acids and Li-ions are continuously increased during the etching process. By implementing the EN-MILD approach, the electrical conductivity increases up to 2.4 × 104 S cm-1, which is the highest reported value to date for Ti3C2Tx MXenes (a traditional MILD approach results in a conductivity of 5.8 × 103 S cm-1). This significant improvement in electrical conductivity arises from the high quality of the synthesized MXene sheets as well as a larger flake size. The EN-MILD synthesis approach also offers high yield of delaminated single MXene layers (up to ∼60% after the first round of washing/centrifugation) and high colloidal concentrations (up to 31 mg ml-1). The working electrode prepared from free-standing MXene paper shows an exceptional capacitance of ≈490 F g-1 at 1 A g-1 in a supercapacitor, which is among the highest values reported for MXene-based supercapacitor electrodes. The exceptional electrical conductivity, high yield of delaminated MXene single layers, and high colloidal concentration of the EN-MILD approach significantly expand the applications of MXenes.
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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".