Active modulation of the plasmonic response is at the forefront of today's research in nano-optics. For a fast and reversible modulation, external magnetic fields are among the most promising approaches. However, fundamental limitations of metals hamper the applicability of magnetoplasmonics in real-life active devices. While improved magnetic modulation is achievable using ferromagnetic or ferromagnetic-noble metal hybrid nanostructures, these suffer from severely broadened plasmonic response, ultimately decreasing their performance. Here we propose a paradigm shift in the choice of materials, demonstrating for the first time the outstanding magnetoplasmonic performance of transparent conductive oxide nanocrystals with plasmon resonance in the near-infrared. We report the highest magneto-optical response for a nonmagnetic plasmonic material employing F- and In-codoped CdO nanocrystals, due to the low carrier effective mass and the reduced plasmon line width. The performance of state-of-the-art ferromagnetic nanostructures in magnetoplasmonic refractometric sensing experiments are exceeded, challenging current best-in-class localized plasmon-based approaches.
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http://dx.doi.org/10.1021/acs.nanolett.2c03383 | DOI Listing |
ACS Appl Mater Interfaces
January 2025
Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto 611-0011, Japan.
The development of hole-collecting materials is indispensable to improving the performance of perovskite solar cells (PSCs). To date, several anchorable molecules have been reported as effective hole-collecting monolayer (HCM) materials for p-i-n PSCs. However, their structures are limited to well-known electron-donating skeletons, such as carbazole, triarylamine, etc.
View Article and Find Full Text PDFJMIR Hum Factors
January 2025
Institute of General Practice, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Background: The internet is a key source of health information, but the quality of content from popular search engines varies, posing challenges for users-especially those with low health or digital health literacy. To address this, the "tala-med" search engine was developed in 2020 to provide access to high-quality, evidence-based content. It prioritizes German health websites based on trustworthiness, recency, user-friendliness, and comprehensibility, offering category-based filters while ensuring privacy by avoiding data collection and advertisements.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
Background: Individuals with hearing impairments may face hindrances in health care assistance, which may significantly impact the prognosis and the incidence of complications and iatrogenic events. Therefore, the development of automatic communication systems to assist the interaction between this population and health care workers is paramount.
Objective: This study aims to systematically review the evidence on communication systems using human-computer interaction techniques developed for deaf people who communicate through sign language that are already in use or proposed for use in health care contexts and have been tested with human users or videos of human users.
Int J Implant Dent
January 2025
Center of Oral Implantology, Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou, China.
Purpose: This systematic review aims to assess the performance, methodological quality and reporting transparency in prediction models for the dental implant's complications and survival rates.
Methods: A literature search was conducted in PubMed, Web of Science, and Embase databases. Peer-reviewed studies that developed prediction models for dental implant's complications and survival rate were included.
Eur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
Aims: Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.
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