Nanocarbon composite materials with optical response on radioactive waste
Bibliographic record
Abstract
Nanocarbon materials have numerous unique features -high porosity, large specific surface area, chemical inertness, radiation stability, etc.We applied nanocarbon/nanodiamond and silicon carbidecomposites as matrix for optical chemochips construction.Composite elements consist of porous nanocarbon substrate with specific chromophores introduced to nanosize pores and siliconorganic coating.Similar multicomponent composition has a response to radiation, in particular, -irradiation.By using diarylethenes as sensitive chromophores, their electronic and/or luminescent spectra data may be applied for doze power detection.Quantum chemistry methods, computer simulation were considered for optimal design of nanocarbon/organic chromophore hybrid.Experimental data and modeling have shown that diarylethenes are able to change color under -irradiation in molecular crystal phase only.Weak interactions (inter-, intramolecular and binding with hydrogen-containing walls in pores) play key role under irradiation.Induced self-organization in limited volume is considered.SWAXS and AFM data are discussed.Nanodiamond composition possesses luminescent response to -, -and -irradiation.Siliconorganic polymer and SiC composition are neutral to irradiation, they are stable in extreme conditions.Application of composite elements with optical sensing as multifunctional chemochip fragment for atmospheric media monitoring is discussed.
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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".