Cu O-polypyrrole conductive aerogel loaded on graphene oxide framework (Cu O-PPy@GO) with a three-dimensional (3D) porous architecture was utilized for high-efficient visual screening of HS on a flexible paper substrate. The detectable signal was acquired on a portable smartphone by using a self-referenced imaging platform equipped with the light emitting diode (LED) accompanying an image processing. As a proof-of-concept, Cu O-PPy@GO aerogel-based sensing strategy was also developed for NaS detection and egg spoilage monitoring. Such a flexible paper-supported sensor is expected for potential application in portable and wearable food-safety fields.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/acs.analchem.8b03011 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Usefulness of the "En face view" method for diagnosing perimembranous ventricular septal defects.
J Echocardiogr
December 2024
Division of Cardiovascular Surgery, Nagano Children's Hospital, Nagano, Japan.
Background: Perimembranous ventricular septal defect (VSD) can be classified as having trabecular, inlet, or outlet extension. The surgical approach used in patch closure depends on the which valve of the tricuspid valve to suture around and the avoidance of the specialized conducting system. This retrospective study evaluated the usefulness of the "En face view" method for classifying perimembranous VSD.
View Article and Find Full Text PDFPrediction of Synchronous Serum CEA Expression Status Based on Baseline MRI Features of Primary Rectal Cancer Lesions Pre-treatment: A Retrospective Study.
Sci Rep
December 2024
Department of Radiology, the Affiliated Taian City Central Hospital of Qingdao University, Tai'an, 271099, China.
This study aimed to investigate the correlation between baseline MRI features and baseline carcinoembryonic antigen (CEA) expression status in rectal cancer patients. A training cohort of 168 rectal cancer patients from Center 1 and an external validation cohort of 75 rectal cancer patients from Center 2 were collected. A nomogram was constructed based on the training cohort and validated using the external validation cohort to predict high baseline CEA expression in rectal cancer patients.
View Article and Find Full Text PDFStructural MRI study of Pareidolia and Visual Hallucinations in Drug-Naïve Parkinson's disease.
Sci Rep
December 2024
Department of Neurology, The Jikei University School of Medicine, 3-25-8 Nishi-Shimbashi, Minato-ku, Tokyo, 105-8461, Japan.
Visual hallucinations (VH) and pareidolia, a type of minor hallucination, share common underlying mechanisms. However, the similarities and differences in their brain regions remain poorly understood in Parkinson's disease (PD). A total of 104 drug-naïve PD patients underwent structural MRI and were assessed for pareidolia using the Noise Pareidolia Test (NPT) were enrolled.
View Article and Find Full Text PDFDevelopment and validation of a mortality predictive model for ICU patients with primary pulmonary hypertension.
Sci Rep
December 2024
Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.
There is a lack of an effective prognostic model for predicting outcomes in patients with primary pulmonary hypertension (PPH). A retrospective analysis was conducted on PPH patients from MIMIC and eICU databases. A predictive model was developed to assess mortality risk.
View Article and Find Full Text PDFCross-domain lung opacity detection via adversarial learning and box fusion.
Sci Rep
December 2024
The Engineering & Technical College of Chengdu University of Technology, Xiaoba Road, Leshan, 614000, China.
Many conditions, such as pulmonary edema, bleeding, atelectasis or collapse, lung cancer, and shadow formation after radiotherapy or surgical changes, cause Lung Opacity. An unsupervised cross-domain Lung Opacity detection method is proposed to help surgeons quickly locate Lung Opacity without additional manual annotations. This study proposes a novel method based on adversarial learning to detect Lung Opacity on chest X-rays.
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!