Water‐based polyurethanes for sustainable advanced manufacture
Bibliographic record
Abstract
Abstract Polyurethanes (PUs) are a class of versatile polymers; their structure and morphology can be readily tailored to exhibit various mechanical, physical, chemical, and biological properties. PUs have been employed in a variety of industrial applications including foams, coatings, textiles, machinery, sporting, transportation, vehicles, and construction. Water‐based PU was developed to reduce the usage of volatile organic compounds (VOCs) in the synthesis of conventional solvent‐based PU, and exhibits advantages of superior material properties (eg, flexibility, stretchability, elasticity, mechanical strength), processability (eg, 3D printing, inkjet printing, screen printing, spray coating, moulding), and sustainability (eg, low VOCs, degradable). Water‐based PU combines the superior mechanical properties of PU with the excellent stability of colloids. The current research trend of PU is shifting from traditional industrial applications to state‐of‐the‐art fields such as soft and wearable electronics, energy storage devices, biosensors, actuators, photovoltaic devices, and stimuli‐responsive materials. Due to the existence of a variety of functional groups such as urethane, aliphatic or aromatic hydrocarbons, esters, ethers, amides, and urea, water‐based PU can be physically or chemically incorporated with functional materials to form PU colloidal composites towards various applications. This paper summarizes the fundamentals of water‐based PU and their colloidal properties and reviews the advanced printing manufacture and the latest explorations of water‐based PU for emerging flexible and stretchable electronics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".