Achieving tunable emissions spanning the spectrum, from blue to near-infrared (NIR) light, within a single component is a formidable challenge with significant implication, particularly in tailoring multicolor luminescence for anti-counterfeiting purposes. In this study, we demonstrate a broad spectrum of emissions, covering blue to red and extending into NIR light in [BPy]CdX : xSb (BPy=Butylpyridinium; X=Cl, Br; x=0 to 0.08) through precise multisite structural fine-tuning. Notably, the multicolor emissions from [BPy]CdBr : Sb manifest a distinctive pattern, transitioning from blue to yellow in tandem with the host [BPy]CdBr and further extending from yellow to NIR with its homologous [BPy]CdCl : Sb, resulting in the simultaneous presence of intersecting and independent emission colors. Detailed modulation of chemical composition enables partial luminescence switching, facilitating the creation of diverse patterns with multicolor luminescence by employing [BPy]CdX : xSb as phosphors. This study for the first time successfully implements several groups of tunable emission colors in a single matrix via multisite fine-tuning. Such an effective strategy not only develops the specific relationships between tunable emissions and adjustable compositions, but also introduces a cost-effective and straightforward approach to achieving unique, high-level, plentiful-color and multiple-information-storage labels for advanced anti-counterfeiting applications.
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http://dx.doi.org/10.1002/anie.202400760 | DOI Listing |
Alzheimers Dement
December 2024
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.
Introduction: Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to the microstructural properties of brain tissues and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest without considering the underlying fiber geometry.
Methods: We propose a novel macrostructure-informed normative tractometry (MINT) framework to investigate how white matter (WM) microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia.
Sleep
November 2024
Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
Study Objectives: This study aimed to (1) improve sleep staging accuracy through transfer learning (TL), to achieve or exceed human inter-expert agreement and (2) introduce a scorability model to assess the quality and trustworthiness of automated sleep staging.
Methods: A deep neural network (base model) was trained on a large multi-site polysomnography (PSG) dataset from the United States. TL was used to calibrate the model to a reduced montage and limited samples from the Korean Genome and Epidemiology Study (KoGES) dataset.
Biotechnol J
August 2024
National and Local Joint Engineering Research Center for Biomanufacturing of Chiral Chemicals, Zhejiang University of Technology, Hangzhou, People's Republic of China.
Angew Chem Int Ed Engl
April 2024
College of Chemistry, Sichuan University, Chengdu, 610065, P. R. China.
Achieving tunable emissions spanning the spectrum, from blue to near-infrared (NIR) light, within a single component is a formidable challenge with significant implication, particularly in tailoring multicolor luminescence for anti-counterfeiting purposes. In this study, we demonstrate a broad spectrum of emissions, covering blue to red and extending into NIR light in [BPy]CdX : xSb (BPy=Butylpyridinium; X=Cl, Br; x=0 to 0.08) through precise multisite structural fine-tuning.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2023
For machine learning applications in medical imaging, the availability of training data is often limited, which hampers the design of radiological classifiers for subtle conditions such as autism spectrum disorder (ASD). Transfer learning is one method to counter this problem of low training data regimes. Here we explore the use of meta-learning for very low data regimes in the context of having prior data from multiple sites - an approach we term site-agnostic meta-learning.
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