A Flexible Nanocomposite Film of Electrochemically Exfoliated Graphene @ Ti<sub>3</sub>CNT<sub><i>x</i></sub> for Supercapacitors with high Volumetric Capacitance
Bibliographic record
Abstract
Interest in developing electrodes for flexible supercapacitors for wearable and portable electronic devices is rising. 2D interfacial heterostructures have garnered significant attention due to their robust structural integrity and excellent electrochemical compatibility. This work demonstrates an innovative and novel electrode combining Ti 3 CNT x and Nitrogen-doped electrochemically exfoliated graphene (N-EEG) for flexible supercapacitors, effectively mitigating self-restacking and enabling enhanced diffusion of electrolyte ions to electroactive sites. The maximum volumetric capacitance of 331 F cm –3 (at a current density of 1 mA cm –2 ) was achieved with an optimized ratio of N-EEG to Ti 3 CNT x, surpassing some reported graphene- or MXene-based supercapacitors. This hybrid electrode retained 93% capacitance after 10 000 charge–discharge cycles, highlighting its durability. A symmetric supercapacitor exhibited a volumetric capacitance of 155 F cm –3 and good stability with ∼100% retention after 10 000 cycles. Such a remarkable performance underscores the potential of the N-EEG@Ti 3 CNT x nanocomposite for the development of supercapacitors.
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 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".