Bibliographic record
Abstract
Abstract Advancements in the synthesis of sulfur‐rich materials are driving progress across diverse fields owing to the rich and tunable functionalities of those materials. These materials are typically valued for their electrochemical behaviors, high refractive indices, heavy metal affinity, and ability to form dynamic covalent bonding. As a result, their applications span various industries including electronics, catalysis, lithium‐sulfur batteries, water reclamation, and optoelectronics. Moreover, elemental sulfur, a byproduct of the petroleum industry, is produced abundantly, necessitating the exploration of novel valorization routes for polymers made from this feedstock. The unique combination of properties of sulfur‐rich polymers also makes them an ideal platform for the development of high‐performance functional coatings, offering durability and tailored functionalities for protective coatings, thus enhancing materials lifespan and performances in a variety of environmental conditions. The presence of dynamic covalent bonds in many sulfur‐rich polymers enables the creation of self‐healing coatings, while sulfur itself or the comonomers can contribute to antimicrobial, antifouling, and corrosion‐resistant properties. Furthermore, sulfur‐rich polymers have the potential to be used in the design of icephobic and superhydrophobic coatings. This underscores the versatility of sulfur‐rich polymers as a platform for the creation of advanced coatings with superior properties.
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 | 0.000 |
| 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) | 0.007 | 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".