Engineered pigment nanoparticles: bottom-up synthesis, characterization, and performance properties
Bibliographic record
Abstract
High performance organic nanopigments are an increasingly important class of functional nanomaterials that possess physical properties ranging between molecular and bulk materials. These nanomaterials have been utilized in a variety of important commercial applications including coatings, printing, information storage, and display technologies [1]. We will discuss our recent work in developing a bottom-up approach for preparation of 3 different classes of high-performance organic nanopigments: 1) azo-laked Pigment Red 57:1 (LAK), 2) quinacridone Pigment Red 122 (QUIN), and 3) azobenzimidazolone (BZI) Pigments Yellow 151 and Red 175. The physical characteristics of the nanopigments were characterized by electron microscopy, dynamic light scattering (DLS), and powder X-ray diffraction (XRD), while their coloristic spectral properties, and dispersion thermal stability were investigated in various matrices. We identified relationships between pigment particle size and their coloristic as well as thermal stability properties for these nanopigments. High resolution transmission electron microscopy (HR-TEM) provided clear images of lattice fringes, and enabled us to determine the crystal orientation of the molecules packed within the QUIN and BZI pigment nanoparticles.
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 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 | 0.000 | 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) | 0.003 | 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 teacher head, 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".