Sodium Hexa-titanate nanocomposites modified with trace amounts of ruthenium displayed enhanced supercapacitance
Bibliographic record
Abstract
Sodium hexa-titanate (Na 2 Ti 6 O 13 ) nanostructures exhibit high electrochemical surface area and unique structural properties. In this study, sodium hexa-titanate nanostructures were grown from titanium (Ti) substrate (Ti/Na 2 Ti 6 O 13 ) and were investigated for supercapacitance by doping oxygen vacancies and modifying with trace amounts of ruthenium (Ru) to form nanostructure composites. An electrochemical reduction method was used for inducing oxygen vacancies into the lattice structure of Ti/Na 2 Ti 6 O 13 nanocomposites to enhance electrochemical and supercapacitance properties. Several Characterization techniques like EDS, XRD and XPS were employed to identify and optimize structure property relationship to achieve supercapacitance. An enhanced specific capacitance of 3300 mF cm −2 g −1 was achieved for Ru–Ti/Na 2 Ti 6 O 13 for Ru ultra-low loading of 0.4 μg of Ru in contrast to a specific capacitance of 100 mF cm −2 g −1 observed for unmodified Ti/Na 2 Ti 6 O 13 . • Modified Sodium Hexa-titanate nanocomposites for enhanced supercapacitance • Ruthenium based doping strategies to enhance supercapacitance • Experimental evidence showing a 33 fold increase in supercapacitance • EDS, XRD and XPS employed to identify and optimize structure property relationship
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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".