Supramolecular Gel-Templated In Situ Synthesis and Assembly of CdS Quantum Dots Gels
Bibliographic record
Abstract
Although many studies have attempted to develop strategies for spontaneously organizing nanoparticles (NPs) into three-dimensional (3D) geometries, it remains a fascinating challenge. In this study, a method for in situ synthesis and self-assembly of a CdS quantum dots (QDs) gel using a Cd supramolecular gel as a scaffold was demonstrated. During the QDs formation process, the Cd ions that constituted the Cd gels served as the precursors of the CdS QDs, and the oleic acid (OA) that ligated with the Cd in the supramolecular gels was capped on the surface of the CdS QDs in the form of carboxylate. The OA-stabilized CdS QDs were in situ synthesized in the entangled self-assembled fibrillar networks (SAFIN) of the Cd gels through reactions between the gelator and H2S. As a result, the QDs exactly replicated the framework of the SAFIN in the CdS QD gels instead of simply assembling along the SAFIN of the supramolecular gels. Moreover, the CdS QDs showed extraordinary sensitivity in the fluorescence detection of IO4 − anions. The facile one-step method developed here is a new approach to assembling nanostructured materials into 3D architectures and has general implications for the design of low molecular mass gelators to bring desired functionality to the developed supramolecular gels.
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".