<scp>Self‐assembled free‐floating</scp> nanomaterials from <scp>sequence‐defined</scp> polymers
Bibliographic record
Abstract
Abstract Sequence‐defined polymers can be programmed to self‐assemble into precise nanostructures for applications in biosensing, drug delivery, optics, and molecular computation. Inspired by the natural self‐assembly processes present in biological protein and DNA systems, sets of molecular design rules have emerged across materials classes as instructions to build a variety of tunable structures. This review highlights recent advances in self‐assembled sequence‐defined and sequence‐specific polymers across peptides, peptoids, DNA, and non‐biological synthetic materials, with a focus on synthesis, assembly processes and overall structure. Specifically, these self‐assembled structures are free‐floating, as such constructs can potentially serve as a platform for the aforementioned applications. Emphasis is placed on the molecular design of polymers that self‐assemble into zero‐dimensional, one‐dimensional, two‐dimensional, or three‐dimensional nanostructures. With the development of automated syntheses and increasing control over self‐assembly, future work may focus on emerging classes of compatible hybrid materials with exciting directions toward new architectures and applications.
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) | 0.000 | 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 | 0.000 |
| 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 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".