Hydrogen Production and Storage: Analysing Integration of Photoelectrolysis, Electron Harvesting Lignocellulose, and Atmospheric Carbon Dioxide-Fixing Biosynthesis
Bibliographic record
Abstract
Green hydrogen from photocatalytic water-splitting and photocatalytic lignocellulosic reforming is a significant proposition for renewable energy storage in global net-zero policies and strategies. Although photocatalytic water-splitting and photocatalytic lignocellulosic reforming have been investigated, their integration is novel. Furthermore, biosynthesis can store the evolved hydrogen and fix the atmospheric carbon dioxide in a biocathode chamber. The biocathode chamber is coupled to the combined photocatalytic water-splitting and lignocellulose oxidation in an anode chamber. This integrated system of anode and biocathode mimics a (bio)electrosynthesis system. A visible solar radiation-driven novel hybrid system comprising photocatalytic water-splitting, lignocellulose oxidation, and atmospheric CO2 fixation is, thus, investigated. It must be noted that there is no technology for reducing atmospheric CO2 concentration. Thus, our novel intensified technology enables renewable and sustainable hydrogen economy and direct CO2 capture from air to confront climate change impact. The photocatalytic anode considered is CdS nanocomposites that give a low absorption onset (200 nm), high absorbance range (200–800 nm), and narrow bandgap (1.58–2.4 V). The biocathode considered is Ralstonia eutropha H16 interfaced with photocatalytic lignocellulosic oxidation and a water-splitting anode. The biocathode undergoes autotrophic metabolism fixing atmospheric CO2 and hydrogen to poly(3-hydroxybutyrate) biosynthesis. As the hydrogen evolved can be readily stored, the electron–hole pair can be separated, increasing the hydrogen evolution efficiency. Although there are many experimental studies, this study for the first time sets the maximum theoretical efficiency target from mechanistic deductions of practical insights. Compared to physical/physicochemical absorption with solvent recovery to capture CO2, the photosynthetic CO2 capture efficiency is 51%. The maximum solar-to-hydrogen generation efficiency is 33%. Lignocelluloses participate in hydrogen evolution by (1–4)-glycosidic bond decomposition, releasing accessible sugar monomers or monosaccharides forming a Cd–O–R bond with the CdS/CdOx nanocomposite surface used as a photocatalyst/semiconductor, leading to CO32− in oxidised carboxylic acid products. Lignocellulose dosing as an oxidising agent can increase the extent of water-splitting. The mechanistic analyses affirm the criticality of lignocellulose oxidation in photocatalytic hydrogen evolution. The critical conditions for success are increasing the alcohol neutralising agent’s strength, increasing the selective (ligno)cellulose dosing, broadening the hybrid nanostructure of the photocatalyst/semiconductor, enhancing the visible-light range absorbance, and increasing the solar energy utilisation efficiency.
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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 | 0.000 | 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".