Low-Temperature Approach to Highly Emissive Copper Indium Sulfide Colloidal Nanocrystals and Their Bioimaging Applications
Bibliographic record
Abstract
We report our newly developed low-temperature synthesis of colloidal photoluminescent (PL) CuInS2 nanocrystals (NCs) and their in vitro and in vivo imaging applications. With diphenylphosphine sulphide (SDPP) as a S precursor made from elemental S and diphenylphosphine, this is a noninjection based approach in 1-dodecanethiol (DDT) with excellent synthetic reproducibility and large-scale capability. For a typical synthesis with copper iodide (CuI) as a Cu source and indium acetate (In(OAc)3) as an In source, the growth temperature was as low as 160 °C and the feed molar ratios were 1Cu-to-1In-to-4S. Amazingly, the resulting CuInS2 NCs in toluene exhibit quantum yield (QY) of ~23% with photoemission peaking at ~760 nm and full width at half maximum (FWHM) of ~140 nm. With a mean size of ~3.4 nm (measured from the vertices to the bases of the pyramids), they are pyramidal in shape with a crystal structure of tetragonal chalcopyrite. In situ (31)P NMR (monitored from 30 °C to 100 °C) and in situ absorption at 80 °C suggested that the Cu precursor should be less reactive toward SDPP than the In precursor. For our in vitro and in vivo imaging applications, CuInS2/ZnS core-shell QDs were synthesized; afterwards, dihydrolipoic acid (DHLA) or 11-mercaptoundecanoic acid (MUA) were used for ligand exchange and then bio-conjugation was performed. Two single-domain antibodies (sdAbs) were used. One was 2A3 for in vitro imaging of BxPC3 pancreatic cancer cells. The other was EG2 for in vivo imaging of a Glioblastoma U87MG brain tumour model. The bioimaging data illustrate that the CuInS2 NCs from our SDPP-based low-temperature noninjection approach are good quality.
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".