Functional Bioinorganic Hybrids from Enzymes and Luminescent Silicon-Based Nanoparticles
Bibliographic record
Abstract
This study reports the preparation of functional bioinorganic hybrid materials exhibiting catalytic activity and photoluminescent properties arising from the combination of enzymes and freestanding silicon-based nanoparticles. The hybrid materials reported herein have potential applications in biological sensing/imaging and theranostics, as they combine long-lived silicon-based nanoparticle photoluminescence with substrate-specific enzymatic activity. Thermal hydrosilylation of undecenoic acid and alkene-terminated poly(ethylene oxide) with hydride-terminated silicon nanocrystals afforded nanoparticles functionalized with a mixed surface made up of carboxylic acid and poly(ethylene oxide) moieties. These silicon-based nanoparticles were subsequently conjugated with prototypical enzymes through the carbodiimide-mediated amide coupling reaction in order to form bioinorganic hybrids that display solubility and photostability in phosphate buffer, photoluminescence (λ max = 630 nm), and enzymatic activity. They were characterized using Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), dynamic light scattering analysis (DLS), photoluminescence spectroscopy, and pertinent enzyme activity assays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".