Recent Advances in the Synthesis of Layered, Double‐Hydroxide‐Based Materials and Their Applications in Hydrogen and Oxygen Evolution
Bibliographic record
Abstract
Abstract Layered double hydroxides (LDHs) consist of brucite‐like layers containing hydroxides of two or more different kinds of metal cations. These positively charged layers are neutralized by exchangeable anions in the interlayer galleries. LDH‐based materials have substantial potential in efficient energy (e.g., H2, O2) generation. Over the last few decades, tremendous progress has been made toward developing LDH‐based materials for H2 and O2 evolution, for which the performance of LDH‐based materials is closely related to the synthesis method. This minireview initially outlines the recent advances in the synthesis of LDH‐based materials. The advantages and challenges of the protocols in tuning the properties of the material are also discussed and highlighted. The application section concentrates on the most recent progress in photocatalytic H2 generation and photocatalytic and electrocatalytic O2 evolution. By taking advantage of the flexible tunability and uniform distribution of metal cations in the brucite‐like layers or the intercalated anions in the interlayer space, LDH‐based materials exhibit attractive properties in the generation of H2 and O2 with advantages such as improved light absorption, enhanced charge separation, better electron transfer, promoted electrode reaction kinetics, and high durability.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".