The existence of hidden complex cavities formed inside a self-assembled nanocrystalline structure is discovered in real-time by using surface plasmon resonance near-field refractive index fingerprinting. Furthermore, computer analysis of the naturally occurring R-G-B interference fringes allowed us to reconstruct the 3D cavity formation and crystallization processes quantitatively. For the case of an aqueous droplet containing 10% by volume of 47 nm Al2O3 nanoparticles, the submicrometer-scale inner cavity peak grows up to 0.5% of the entire crystallized crust height of over 150 microm. The formation of the complex inner structure was found to be attributable to multiple cavity inceptions and their competing growth during the aquatic evaporation. This outcome provides a better understanding and feasible control of the formation of nanocrystalline inner structures.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/la802068g | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
[Development of prognostic clinical and genetic models of the risk of low bone mineral density using neural network training].
Probl Endokrinol (Mosk)
January 2024
Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.
View Article and Find Full Text PDFFilling data analysis gaps in time-resolved crystallography by machine learning.
Struct Dyn
January 2025
Department of Physics, University of Wisconsin-Milwaukee, 3135 N. Maryland Ave, Milwaukee, Wisconsin 53211, USA.
There is a growing understanding of the structural dynamics of biological molecules fueled by x-ray crystallography experiments. Time-resolved serial femtosecond crystallography (TR-SFX) with x-ray Free Electron Lasers allows the measurement of ultrafast structural changes in proteins. Nevertheless, this technique comes with some limitations.
View Article and Find Full Text PDFSelecting a smartwatch for trials involving older adults with neurodegenerative diseases: A researcher's framework to avoid hidden pitfalls.
J Biomed Inform
January 2025
Centre for Elderly and Nursing Home Medicine, Department of Global Public Health and Primary Care, University of Bergen, Årstadveien 17 5009 Bergen, Norway; Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas vei 65 5021 Bergen, Norway.
Background: Increased prevalence of neurodegenerative diseases complicates care needs for older adults. Sensing technologies, such as smartwatches, are one available solution which can help address the challenges of aging. Knowledge of the possibilities and pitfalls of these sensing technologies is of key importance to researchers when choosing a device for a trial and considering the sustainability of these technologies in real-world settings.
View Article and Find Full Text PDFPharmaceutical Humanities and Narrative Pharmacy: An Emerging New Concept in Pharmacy.
Pharmaceuticals (Basel)
January 2025
Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany.
The complexity of our life experiences and the rapid progress in science and technology clearly necessitate reflections from the humanities. The ever-growing intersection between science and society fosters the emergence of novel interdisciplinary fields of research. During the past decade, Medical Humanities arose to meet the need to unravel hidden information beyond technology-driven and fact-based medicine.
View Article and Find Full Text PDFResearch on Fire Detection of Cotton Picker Based on Improved Algorithm.
Sensors (Basel)
January 2025
College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model.
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!