Bibliographic record
Abstract
The demand for polymers derived from renewable materials is increasing due to the rising demand for polymer sustainability. Producing commodity polymers using biobased and biodegradable feedstock often yields underperforming products relative to petroleum-based equivalents due to formulation changes. In many cases, the drawbacks of polymers produced from renewable materials (e.g., application properties, ease of production, and cost) are still too great for their commercial adoption. In this work, 12 wt % of the monomer used in a latex-based pressure-sensitive adhesive (PSA) was replaced with starch nanoparticles (SNPs). To reduce the potential for negative effects on the adhesive properties, the SNPs were encapsulated as latex particle cores within acrylic shells (which govern PSA properties), made from a model system to yield an ∼80% biosourced PSA. The core–shell latexes, produced via seeded semi-batch emulsion polymerization, yielded bimodal particle size distributions, which played an important role in PSA performance. To address the changes in PSA performance, cellulose nanocrystals (CNCs) were incorporated. The challenges of combining SNPs and CNCs in an emulsion-based PSA were overcome, and the SNP and CNC loadings were varied to tune the adhesive properties of the PSA films within the range of various commercial tapes.
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 | 0.001 |
| 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".