Versatile and High-Throughput Strategy for the Quantification of Proteins Bound to Nanoparticles
Bibliographic record
Abstract
Biofunctionalized nanomaterials have been extensively studied as a tool for a wide range of applications in biomedical fields. Despite many existing strategies to conjugate proteins to colloidal particles, determining the grafting efficiency—that is, the amount of protein conjugated to the surface of a nanoparticle (NP)—remains challenging. Formulations for biomedical applications are subjected to strict constraints, and a lack of precise characterization can prevent otherwise promising formulations to be explored further. Here, we propose a simple approach to precisely measure the grafting efficiency of biological molecules on the surface of three types of widely used NPs: polymeric NPs, inorganic NPs, and metallic NPs. This approach relies on the simultaneous hydrolysis of the grafted protein and the NP degradation in acidic conditions, followed by a spectrophotometric quantification of primary amines in solution. This strategy can be applied to any type of protein and does not require any labeling agent. It can be performed in a high-throughput manner as a routine experiment and only requires a conventional oven and a microplate reader.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".