Strong and fast-recovery organic/inorganic hybrid AuNPs–supramolecular gels based on loofah-like 3D networks
Bibliographic record
Abstract
Super strong and fast-recovery organic/inorganic hybrid gold nanoparticle (AuNPs)-supramolecular gels based on a three-dimensional loofah-like nanoscale network self-assembled by polyhedral oligomeric silsesquioxane (POSS) core supramolecular gelators are reported for the first time. Two series of POSS core organic/inorganic hybrid gelators, POSS-BOC-l-Homophenylalanine (POSS-Hpy) and POSS-Boc-Cys(Bzl)-OH (POSS-Cys), with two types of peripherals having different abilities for driving the self-assembly of AuNPs in gels were designed and synthesized, both of which self-assembled into three-dimensional loofah-like nanoscale gel networks producing hybrid physical gels with fast-recovery behaviors. The mechanical properties of the resultant hybrid gels were dramatically increased by as much as 100 times in the system of sulfur containing POSS-Cys gelators without destroying the fast-recovery behaviors, with the addition of AuNPs, which had direct interaction with AuNPs to give S-Au non-covalent driving force to lead AuNPs self-assemble onto the 3D loofah-like network nanofibres in the supramolecular hybrid gel system. However, in the POSS-Hpy gelator system without sulfur, no strong interaction with AuNPs existed and the POSS-Hpy nanocomposites showed no clear changes in morphology, thermal stability or rheological properties, confirmed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), tube-inversion and rotational rheometer measurements. This indicated that the organic/inorganic hybrid gelator POSS-Cys could be applied to the formation of soft materials in which AuNPs were self-assembled and closely arranged into three-dimensional nanoscale networks. This hybrid material has great potential for applications in self-recovery, nano- and micron-scale electronic devices, because it has both a large mechanical strength and a fast-recovery capability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".