High surface area silicon materials: fundamentals and new technology
Bibliographic record
Abstract
Crystalline silicon forms the basis of just about all computing technologies on the planet, in the form of microelectronics. An enormous amount of research infrastructure and knowledge has been developed over the past half-century to construct complex functional microelectronic structures in silicon. As a result, it is highly probable that silicon will remain central to computing and related technologies as a platform for integration of, for instance, molecular electronics, sensing elements and micro- and nanoelectromechanical systems. Porous nanocrystalline silicon is a fascinating variant of the same single crystal silicon wafers used to make computer chips. Its synthesis, a straightforward electrochemical, chemical or photochemical etch, is compatible with existing silicon-based fabrication techniques. Porous silicon literally adds an entirely new dimension to the realm of silicon-based technologies as it has a complex, three-dimensional architecture made up of silicon nanoparticles, nanowires, and channel structures. The intrinsic material is photoluminescent at room temperature in the visible region due to quantum confinement effects, and thus provides an optical element to electronic applications. Our group has been developing new organic surface reactions on porous and nanocrystalline silicon to tailor it for a myriad of applications, including molecular electronics and sensing. Integration of organic and biological molecules with porous silicon is critical to harness the properties of this material. The construction and use of complex, hierarchical molecular synthetic strategies on porous silicon will be described.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".