The order parameters of a number of different solutes dissolved in a number of different nematic liquid-crystal solvents are measured and compared. It is shown that the order parameters can all be rationalized with the ansatz that there exist two independent ordering mechanisms operating in the liquid crystals employed. With this ansatz it is possible to fit the experimental order parameters to better than 5%. This opens the possibility of the accurate prediction of order parameters in ordered liquids.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1063/1.2900559 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Bayesian Borrowing With Multiple Heterogeneous Historical Studies Using Order Restricted Normalized Power Prior.
Stat Med
February 2025
U.S. Food and Drug Administration, Silver Spring, Maryland.
The recent U.S. Food and Drug Administration guidance on complex innovative trial designs acknowledges the use of Bayesian strategies to incorporate historical information based on clinical expertise and data similarity.
View Article and Find Full Text PDFPhytoremediation of Oil-Contaminated Soil by L. Combined with Biochar and Microbial Agent.
Plants (Basel)
January 2025
College of Life Sciences, Nankai University, Tianjin 300071, China.
Crude oil pollution of soil is an important issue that has serious effects on both the environment and human health. Phytoremediation is a promising approach to cleaning up oil-contaminated soil. In order to facilitate phytoremediation effects for oil-contaminated soil, this study set up a pot experiment to explore the co-application potentiality of L.
View Article and Find Full Text PDFDetailed Determination of Delamination Parameters in a Multilayer Structure Using Asymmetric Lamb Wave Mode.
Sensors (Basel)
January 2025
Ultrasound Research Institute, Kaunas University of Technology, LT-51423 Kaunas, Lithuania.
A signal-processing algorithm for the detailed determination of delamination in multilayer structures is proposed in this work. The algorithm is based on calculating the phase velocity of the Lamb wave A mode and estimating this velocity dispersion. Both simulation and experimental studies were conducted to validate the proposed technique.
View Article and Find Full Text PDFAutomated Volumetric Milling Area Planning for Acoustic Neuroma Surgery via Evolutionary Multi-Objective Optimization.
Sensors (Basel)
January 2025
School of Biomedical Engineering, Tsinghua University, Shuang Qing Road, Beijing 100084, China.
Mastoidectomy is critical in acoustic neuroma surgery, where precise planning of the bone milling area is essential for surgical navigation. The complexity of representing the irregular volumetric area and the presence of high-risk structures (e.g.
View Article and Find Full Text PDFEstablishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning.
Sensors (Basel)
January 2025
College of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China.
Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!