The Roles of Biofilm Conductivity and Donor Substrate Kinetics in a Mixed-Culture Biofilm Anode
Bibliographic record
Abstract
We experimentally assessed the kinetics and thermodynamics of electron transfer (ET) from the donor substrate (acetate) to the anode for a mixed-culture biofilm anode. We interpreted the results with a modified biofilm-conduction model consisting of three ET steps in series: (1) intracellular ET, (2) non-Ohmic extracellular ET (EET) from an outer membrane protein to an extracellular cofactor (EC), and (3) ET from the EC to the anode by Ohmic-conduction in the biofilm matrix. The steady-state current density was 0.82 ± 0.03 A/m 2 in a miniature microbial electrochemical cell operated at fixed anode potential of −0.15 V versus the standard hydrogen electrode. Illumina 16S-rDNA and -rRNA sequences showed that the Geobacter genus was less than 30% of the community of the biofilm anode. Biofilm conductivity was high at 2.44 ± 0.42 mS/cm, indicating that the maximum current density could be as high as 270 A/m 2 if only Ohmic-conduction EET was limiting. Due to the high biofilm conductivity, the maximum energy loss for Ohmic-conduction EET was negligible, 0.085 mV. The energy loss in the second ET step also was small, only 20 mV, and the potential for the EC involved in the second ET was −0.15 V, a value documenting that >99% of the EC was in the oxidized state. Monod kinetics for utilization of acetate were relatively slow, and at least 87% of the energy loss was in the intracellular step. Thus, intracellular ET was the main kinetic and thermodynamic bottleneck to ET from donor substrate to the anode for a highly conductive biofilm.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".