Immobilization of Biomolecules in Sol–Gels: Biological and Analytical Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The encapsulation or generation of new surfaces that can fix biomolecules firmly without altering their original conformations and activities is still challenging for the utilization of biochemical functions of active biomolecules. Presently, sol–gel chemistry offers new and interesting possibilities for the promising encapsulation of heat-sensitive and fragile biomolecules (enzyme, protein, antibody and whole cells of plant, animal and microbes); mainly, it is an inherent low temperature process and biocompatible. The typical sol–gel process initiates by the hydrolysis of M(OR) 4 and is performed in the presence of the active biomolecule. Hydrolysis and condensation of the M-monomers in the presence of an acid or base catalyst trigger cross-linking with formation of amorphous MO 2 , a porous inorganic matrix that grows around the biomolecule in a three-dimensional manner. This class of sol–gel matrices possesses chemical inertness, physical rigidity, negligible swelling in aqueous solution, tunable porosity, high photochemical and thermal stability, and optical transparency. These attractive features have led to intense research in the optical and electrochemical biosensors, which may be useful for medical, environmental and industrial applications. On the other hand, sol–gel encapsulated organelles have been transplanted to the living systems, and plant/animal/microbial cells have also been employed for the production of commercially important metabolites. This review article highlights the advantages, recent developments, applications and future perspectives of sol–gel immobilized biomolecules, which includes enzymes, antibodies, microorganisms, plant and animal cells.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it