Nanostructured interfaces for probing and facilitating extracellular electron transfer
Bibliographic record
Abstract
Extracellular electron transfer (EET) is a process performed by electrochemically active bacteria (EAB) to transport metabolically-generated electrons to external solid-phase acceptors through specific molecular pathways. Naturally bridging biotic and abiotic charge transport systems, EET offers ample opportunities in a wide range of bio-interfacing applications, from renewable energy conversion, resource recovery, to bioelectronics. Full exploration of EET fundamentals and applications demands technologies that could seamlessly interface and interrogate with key components and processes at relevant length scales. In this review, we will discuss the recent development of nanoscale platforms that enabled EET investigation from single-cell to network levels. We will further overview research strategies for utilizing rationally designed and integrated nanomaterials for EET facilitation and efficiency enhancement. In the future, EET components such as c-cytochrome based outer membranes and bacterial nanowires along with their assembled structures will present themselves as a whole new category of biosynthetic electroactive materials with genetically encoded functionality and intrinsic biocompatibility, opening up possibilities to revolutionize the way electronic devices communicate with biological systems.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".