Platinum‐Doped Carbon Nitride‐Loaded Poly( <i>N</i> ‐Isopropylacrylamide) Hydrogel Thin Films for Green Hydrogen Production Systems: Morphological Study for Higher Efficiency
Bibliographic record
Abstract
Photocatalytic water splitting enables the generation of green hydrogen (H 2 ). In this framework, water and sunlight are the sustainable sources. Photocatalyst‐loaded hydrogel materials have already shown their potential as a water storage and catalyst host matrix for H 2 production. This study explores the thin film geometry of such systems to demonstrate the scalability of photocatalysis. Graphitic carbon nitride is used as a catalyst and combined with platinum as a co‐catalyst. The resulting catalytic centers are introduced in poly( N ‐isopropylacrylamide) hydrogel thin films. First, the swelling behavior of the resulting hybrid hydrogels is studied under high relative humidity, and the influence of different catalyst loadings is discussed. Then, time‐of‐flight neutron reflectometry is used to access the vertical material composition inside the hybrid thin film in the dry state, which shows an enrichment layer of catalyst at the substrate–bulk interface. Operando grazing incidence small‐angle neutron scattering displays the microscopic changes happening under heavy water (D 2 O) vapor and light irradiation. Next, gas chromatography demonstrates the potential of the studied hydrogel films by determining their H 2 production rates. The recorded H 2 production is correlated to the microstructure analysis and reveals the importance of the observed catalyst enrichment layer.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".