A facile roll-to-roll method is developed for fabricating reduced graphene oxide (rGO)-based flexible transparent conductive films. A Sn /ethanol reduction system and a rationally designed fast coating-drying-washing technique are proven to be highly efficient for low-cost continuous production of large-area rGO films and patterned rGO films, extremely beneficial toward the manufacture of flexible photoelectronic devices.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/adma.201605028 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
View Article and Find Full Text PDFArtificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare.
Health Sci Rep
January 2025
Department of Research The Medical Research Circle (MedReC) Goma Democratic Republic of the Congo.
Background And Aim: Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy.
View Article and Find Full Text PDFArtificial intelligence in healthcare: transforming patient safety with intelligent systems-A systematic review.
Front Med (Lausanne)
January 2025
Department of Law, Institute of Legal Medicine, University of Macerata, Macerata, Italy.
Introduction: Adverse events in hospitals significantly compromise patient safety and trust in healthcare systems, with medical errors being a leading cause of death globally. Despite efforts to reduce these errors, reporting remains low, and effective system changes are rare. This systematic review explores the potential of artificial intelligence (AI) in clinical risk management.
View Article and Find Full Text PDFNext-gen agriculture: integrating AI and XAI for precision crop yield predictions.
Front Plant Sci
January 2025
Department of Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing (III TDM), Kurnool, Andhrapradesh, India.
Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors.
View Article and Find Full Text PDFTimely access to specialist outpatient care: can applying systems thinking unblock our waiting lists?
BMC Health Serv Res
January 2025
Centre for the Business and Economics of Health (CBEH), The University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia.
Background: The purpose of this qualitative study was to focus on review and repeat review outpatients and the structural role they play in exacerbating waitlists for Specialist Outpatient (SOP) services in Queensland. Waitlists, which record the number of patients waiting for an initial consultation (new appointment), are an indicator of a health system under strain. Waiting too long to access SOP can have a detrimental effect on people's health outcomes.
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!