Surface Activity of Highly Hydrophobic Surfactants and Platelike PbSe and CuSe Nanoparticles
Bibliographic record
Abstract
Lead selenide (PbSe) and copper selenide (CuSe) nanoparticles were synthesized in aqueous phase at a relatively mild temperature (85 °C) in the presence of various cationic Gemini surfactants (12−2−12, 12−0−12, and 16−2−16) as capping/stabilizing agents. All nanoparticles exhibited clear core−shell (surfactant) morphologies. PbSe reactions produced predominantly platelike cubic morphologies along with long Se nanorods (NRs) as a reaction byproduct. CuSe particles were polyhedral thin plates with perforations. High resolution transmission electron microscopy (HRTEM), field emission scanning electron microscopy (FESEM), and X-ray diffraction (XRD) measurements were used to characterize the shape and structure of the particles. HRTEM allowed us to measure the precise thickness of the surfactant shell around each nanocrystal (NC) which was in excellent agreement with the length of the surfactant hydrocarbon tail. Infrared spectroscopic (FT-IR) studies suggested a strong affinity of cationic surfactant for NC surface which was the driving force for the monolayer formation in the form of a shell. Energy dispersive X-ray spectroscopic (EDS) analysis demonstrated that PbSe and CuSe particles were always in 1:1 stoichiometry, and Se NRs were made up of only pure Se and no Pb contents were observed. Stronger interfacial adsorption of a surfactant with greater hydrophobicity controlled the morphology to produce platelike geometries. The size of PbSe and CuSe particles increased while the thickness decreased as the hydrophobicity of the surfactant increased in the order of 12−2−12 < 12−0−12 < 16−2−16.
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".