Electroactive Nanoporous Metal Oxides and Chalcogenides by Chemical Design
Bibliographic record
Abstract
Abstract The archetypal silica- and aluminosilicate-based zeolite-type materials are renowned for wide-ranging applications in heterogeneous catalysis, gas-separation and ion-exchange. Their compositional space can be expanded to include nanoporous metal chalcogenides, exemplified by germanium and tin sulfides and selenides. By comparison with the properties of bulk metal dichalcogenides and their 2D derivatives, these open-framework analogues may be viewed as three-dimensional semiconductors filled with nanometer voids. Applications exist in a range of molecule size and shape discriminating devices. However, what is the electronic structure of nanoporous metal chalcogenides? Herein, materials modeling is used to describe the properties of a homologous series of nanoporous metal chalcogenides denoted np-MX2, where M = Si, Ge, Sn, Pb, and X = O, S, Se, Te, with Sodalite, LTA and aluminum chromium phosphate-1 structure types. Depending on the choice of metal and anion their properties can be tuned from insulators to semiconductors to metals with additional modification achieved through doping, solid solutions, and inclusion (with fullerene, quantum dots, and hole transport materials). These systems form the basis of a new branch of semiconductor nanochemistry in three dimensions.
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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 | 0.001 |
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".