Review of Hybrid 1D/2D Photocatalysts for Light-Harvesting Applications
Bibliographic record
Abstract
Among the 17 Sustainable Development Goals (SDGs), we find that SDG7 (clean and affordable energy) and SDG13 (climate action) need to be urgently addressed, also because they are intimately related. Population growth, which has followed an exponential curve for the past two centuries, makes these challenges particularly daunting because growth in energy demand follows the same trend. A variety of renewable energy technologies have been developed to meet the growing need for energy and simultaneously reduce greenhouse gas emissions. Among these, solar energy is considered to be particularly promising because of the relative abundance of sunlight and the progressive reduction in cost. Recent efforts have also focused on developing nanostructured photocatalysts because of their potential for high-performance sustainable energy technologies. Here we review the properties of hybrid one-dimensional/two-dimensional (1D/2D) photocatalysts, which are being widely studied because of several interesting features, including structural (particle/crystallite size, phase composition, specific surface area, surface hydroxyls, and lattice defects) and physical/chemical (chemical stability and optoelectronic, electromagnetic, and photophysical properties) properties. By tuning of the band gap and optimization of the recombination rate of photogenerated charge carriers, these systems can optimize solar energy harvesting beyond the visible spectrum, resulting in an enhanced photocatalytic performance. In particular, we will discuss the following subtopics: classification of the materials’ architecture, synthesis techniques, prominent factors that influence the photocatalytic activity, prospects in energy conversion and storage (e.g., H 2 production), bacterial disinfection, pollutant degradation, batteries, and CO 2 conversion into value-added chemicals. We conclude by describing broad perspectives on future studies of 1D/2D photocatalysts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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 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".