Single-molecule protein arrays enabled by scanning probe block copolymer lithography
Bibliographic record
Abstract
The ability to control the placement of individual protein molecules on surfaces could enable advances in a wide range of areas, from the development of nanoscale biomolecular devices to fundamental studies in cell biology. Such control, however, remains a challenge in nanobiotechnology due to the limitations of current lithographic techniques. Herein we report an approach that combines scanning probe block copolymer lithography with site-selective immobilization strategies to create arrays of proteins down to the single-molecule level with arbitrary pattern control. Scanning probe block copolymer lithography was used to synthesize individual sub-10-nm single crystal gold nanoparticles that can act as scaffolds for the adsorption of functionalized alkylthiol monolayers, which facilitate the immobilization of specific proteins. The number of protein molecules that adsorb onto the nanoparticles is dependent upon particle size; when the particle size approaches the dimensions of a protein molecule, each particle can support a single protein. This was demonstrated with both gold nanoparticle and quantum dot labeling coupled with transmission electron microscopy imaging experiments. The immobilized proteins remain bioactive, as evidenced by enzymatic assays and antigen-antibody binding experiments. Importantly, this approach to generate single-biomolecule arrays is, in principle, applicable to many parallelized cantilever and cantilever-free scanning probe molecular printing methods.
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) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".