Pushing the methodological envelope in understanding the photo/electrosynthetic materials-microorganism interface
Bibliographic record
Abstract
Biohybrid photo/electrosynthetic systems synergize microbial metabolic pathways and inorganic materials to generate the fuels and chemicals to power our society. They aim to combine the strengths of product selectivity from biological cells and efficient charge generation and light absorption of inorganic materials. However crucial mechanistic questions still remain. In this review we address significant knowledge gaps that must be closed and recent efforts to do so to push biohybrid systems closer to applicability. In particular, we focus on noteworthy advances that have recently been made in applying state-of-the-art analytical spectroscopic, electrochemical, and microelectronic techniques to help pinpoint key complexities of the microbe-materials interface. We discuss the basic function of these techniques, how they have been translated over to study biohybrid systems, and which key insights and implications have been extracted. Finally, we delve into the key advances necessary for the design of next generation biohybrid energy conversion systems.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".