First Hyperpolarizability of Carbonaceous Nanoparticles
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Bibliographic record
Abstract
Second order non-linear optical materials have applications in telecommunications, optical switching, and signal processing. The first hyperpolarizability (β) data at the molecular level is needed to quantify their potential in applications. In this work, the first hyperpolarizability was measured for a new class of materials known as carbon nanoparticles via second harmonic light scattering technique (SHLS) in solution. These nanoparticles were first reported by Xu et al. 1 in 2004 and have subsequently been prepared in the laboratory by various synthetic methods. We have prepared two different sizes (26 and 35 nm) by hydrothermal method using glucose as a precursor. Since the carbon nanoparticles contain a significant amount of oxygen, nitrogen, and hydrogen along with carbon, we call them carbonaceous nanoparticles (CNPs). The second order nonlinearity of CNPs have not been reported and our experiments show that CNPs scatter second harmonic (SH) light, and their first hyperpolarizability (β) lies in the order of 10-27 esu per nanoparticle which is 102 times higher than that of para-nitroaniline but lower by a factor of 103 than that of gold nanoparticles. The monochromator scan of second harmonic light scattered from CNPs in aqueous solution shows a broad peak arising from multi photon fluorescence (MPF) along with a sharp second harmonic peak. We have employed a spectral fitting procedure to eliminate the MPF contribution to extract β from the normalized SH intensity 2. The second harmonic response originates from the surface of the CNPs since the bulk is more or less centrosymmetric and cannot contribute to SH scattering in a big way. I will also present how protein adsorption on CNPs change the SH light scattering intensity and show how the adsorption of proteins on the CNP surface can be modelled using various adsorption isotherms. © 2023, International ASET Inc. All rights reserved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it