Polymer‐Grafted, Gold Nanoparticle‐Based Nano‐Capsules as Reversible Colorimetric Tensile Strain Sensors
Bibliographic record
Abstract
Abstract Colloidal colorimetric microsensors enable the in‐situ detection of mechanical strains within materials. Enhancing the sensitivity of these sensors to small scale deformation while enabling reversibility of the sensing capability would expand their utility in applications including biosensing and chemical sensing. In this study, we introduce the synthesis of colloidal colorimetric nano‐sensors using a simple and readily scalable fabrication method. Colloidal nano sensors are prepared by emulsion‐templated assembly of polymer‐grafted gold nanoparticles (AuNP). To direct the adsorption of AuNP to the oil‐water interface of emulsion droplets, AuNP (≈11nm) are functionalized with thiol‐terminated polystyrene (PS, Mn = 11k). These PS‐grafted gold nanoparticles are suspended in toluene and subsequently emulsified to form droplets with a diameter of ≈30µm. By evaporating the solvent of the oil‐inwater emulsion, we form nanocapsules (AuNC) (diameter < 1µm) decorated by PS‐grafted AuNP. To test mechanical sensing, the AuNC are embedded in an elastomer matrix. The addition of a plasticizer reduces the glass transition temperature of the PS brushes, and in turn imparts reversible deformability to the AuNC. The plasmonic peak of the AuNC shifts towards lower wavelengths upon application of uniaxial tensile tension, indicating increased inter‐nanoparticle distance, and reverts back as the tension is released.
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".