Surface modification by assembling: a modular approach based on the match in nanostructures
Bibliographic record
Abstract
Quantitative co-immobilization of multiple bioactive proteins or diverse chemical moieties on a surface is challenging because of the competition among the reactants. In this work, a two-step method is proposed, in which each type of reactant is firstly grafted onto the surface of functional nanoparticles, and then these nanoparticles are mixed and cast onto a substrate that has an appropriate nano-topography to trap and immobilize the nanoparticles. This approach has two distinct advantages: (1) it avoids competition among reactants of different natures; (2) the nanoparticles prepared in the first step can be physically mixed at the desired quantity and time and with the desired type. To demonstrate the feasibility, human and bovine albumins as model molecules were covalently immobilized onto the surface of reactive polypyrrole (PPy) nanoparticles, separately; and then the two types of protein grafted particles were mixed at various ratios in aqueous solution and cast onto the nanotubular surface of a PPy membrane. On drying, the nanotubes on the membrane surface shrunk and "locked" the particles, forming a stable bifunctional surface. Considering the availability of a wide range of nanoparticles and the technical capability to construct nanostructured substrates, this strategy may provide a general solution to quantitatively immobilize multiple biomolecules or chemical moieties. In particular, the electrically conductive and multi-biofunctional flexible PPy membrane demonstrated in this work can be a useful material platform for biomedical applications such as multi-target biosensing and targeted electrical stimulation.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".