Laccase–biosilica nanostructures — A miniaturized automatic approach
Bibliographic record
Abstract
In the present work, an automatic generic tool for performing different syntheses of biosilica nanoparticles and the encapsulation of enzymes at the same time is described. Sequential injection analysis (SIA) allowed automation, since it enables the precise and exact control of fluidic manipulations as well as reaction conditions essential for achieving repeatable and reproducible hydrolysis, nucleation, and particle growth. An effective computer control of all the analytical parameters during run time ensured the testing of different templates, silicic acid precursors, and reaction conditions (flow rates, flow reversal, mixing, order of reagents added, pH, etc.) without physical reconfiguration of the flow setup. The effect of tetramethyl orthosilicate, sodium silicate, polyethylenimine, and protamine was evaluated not only for the morphology and size of obtained nanoparticles, but also for the stability and consequently the activity of laccase, the enzyme selected for this demonstration. This activity was evaluated using the spectrophotometric measurement (at 415 nm) of the 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) cationic radical, which results from the action of the encapsulated enzyme. The results obtained showed advantages, namely, reproducibility between all the samples used when compared with the small-scale batch-based process, and the absence of clogging due to the operational characteristics of the SIA technique. Besides the benign reaction conditions, such as ambient temperatures, physiological pH range, and aqueous solvents, this automatic procedure was shown to be a rapid, simple, and more sensitive alternative method for the enzyme immobilization that results in the physical entrapment of enzymes within silica nanospheres as they are formed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".