Functionalization of Contacted Carbon Nanotube Forests by Dip Coating for High‐Performance Biocathodes
Bibliographic record
Abstract
Abstract This work focuses on the use of electrically contacted carbon nanotube forests as an electrode material for the bioelectrocatalytic reduction of oxygen to water. The forests are directly grown by chemical vapor deposition on a conductive tantalum layer, which provides enough mechanic stability during several functionalization and enzyme immobilization steps. A pyrene bis‐anthraquinone derivative (pyr‐(AQ)2) was attached via π‐stacking throughout the forest and was used as an anchor molecule for oriented immobilization of laccase. This led to high‐performance biocathodes for oxygen reduction via direct electron transfer with absolute maximum current densities up to 0.84 mA cm−2 at 0.2 V vs Ag/AgCl. The morphological changes during the wet chemical processes were studied by scanning electron microscopy (SEM) revealing cellular patterning of the forest structure. Despite these changes, the forest remained attached and electrically connected to the tantalum layer. The resulting bioelectrodes performed with satisfying stabilities under constant discharge conditions and kept 75 % of its initial performances after one week.
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".