Membranes for bioelectrochemical systems: challenges and research advances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Increasing energy demand has been a big challenge for current society, as the fossil fuel sources are gradually decreasing. Hence, development of renewable and sustainable energy sources for the future is considered one of the top priorities in national strategic plans. Bioenergy can meet future energy requirements - renewability, sustainability, and even carbon-neutrality. Bioenergy production from wastes and wastewaters is especially attractive because of dual benefits of energy generation and contaminant stabilization. There are several bioenergy technologies using wastes and wastewaters as electron donor, which include anaerobic digestion, dark biohydrogen fermentation, biohydrogen production using photosynthetic microorganisms, and bioelectrochemical systems (BESs). Among them BES seems to be very promising as we can produce a variety of value-added products from wastes and wastewaters, such as electric power, hydrogen gas, hydrogen peroxide, acetate, ethanol etc. Most ofthe traditional BES uses a membrane to separate the anode and cathode chamber, which is essential for improving microbial metabolism on the anode and the recovery of value-added products on the cathode. Performance of BES lacking a membrane can be seriously deteriorated, due to oxygen diffusion or substantial loss of synthesized products. For this reason, usage of a membrane seems essential to facilitate BES performance. However, a membrane can bring several technical challenges to BES application compared to membrane-less BES. These challenges include poor proton permeability, substrate loss, oxygen back diffusion, pH gradient, internal resistance, biofouling, etc. This paper aims to review the major technical barriers associated with membranes and future research directions for their application in BESs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it