Design and Synthesis of Hybrid PEGylated Metal Monopicolinate Cyclam Ligands for Biomedical Applications
Bibliographic record
Abstract
Abstract In this study, we report, for the first time, the synthesis of two original nanosystems, based on gold Au(III) and copper Cu(II): simple gold–copper nanoparticles (Cu0AuNPs) and enriched monopicolinate cyclam (L1)–Cu(II)–Au(III)-complex (L1@Cu2+AuNPs). The two nanomaterials differ substantially by the chelation or not of the Cu(II) ions during the NPs synthesis process. The two hybrid nanoparticles (Cu0AuNPs; L1@Cu2+AuNPs) were deeply studied from the chemical and physical point of view, using many different analytical techniques such as Raman and UV–vis spectroscopy, electron transmission microscopy, and dynamic light scattering. Both nanosystems show morphological and good chemical stability at pH 4 values and in physiological conditions during 98 h. Undifferentiated and neural differentiated murine embryonic stem cells were used as a model system for in vitro experiments to reveal the effects of NPs on these cells. The comparative study between Cu0AuNPs and L1@Cu2+AuNPs highlights that copper chelated in its +2 oxidation state in the NPs is more functional for biological application.
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 | 0.001 |
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".