Lattice engineered nanoscale Fe0 for selective reductions
Bibliographic record
Abstract
Achieving rapid and highly selective chemical reductions using Fe0 nanomaterials for water treatment remains challenging. Here lattice Ni and S were impregnated into crystalline Fe0 with controllable lattice strain and S speciation via a one-step procedure, overcoming the reactivity–selectivity–stability trade-off. Chemoselective dehalogenation and hydrogenation at a remarkable activity (up to 956-fold higher than for unmodified Fe0) outcompete H2 evolution for >90% electrons from lattice-doped Fe0, also offering high stability in air and water. This mainly results from the modulations of materials’ lattice strain (contracted or tensile) and S speciation (FeS or FeS2) by lattice Ni and the promotions of electron transfer and hydrophobicity by lattice S. This work demonstrates the ability to control the local microenvironment in the Fe0 crystalline structure via lattice engineering, and the tunable geometric and electronic properties constitute a promising platform for the rational design of metallic nanomaterials with robust performance in selective reductions. Fe0-enabled nanotechnologies for the reduction of refractory organic contaminants have the limitations of poor selectivity and low stability during water treatment. A lattice doping technique based on Lewis acid–base chemistry to incorporate lattice Ni and S into crystalline Fe0 can achieve rapid and highly selective chemical reductions.
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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".