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Mechanisms of intergranular corrosion and self-healing in high temperature aged lean duplex stainless steel 2404
This study investigated the intergranular corrosion mechanism of lean duplex stainless steel 2404 after long-term aging at 700 and 800 °C using electrochemical methods, thermodynamic calculations, and kinetic models. At 700 °C, σ phase growth significantly increases the degree of sensitization (DOS) and decreases the breakdown potential (Eb). At 800 °C, a self-healing process at the ferrite/σ interface helps recover Cr and Mo depleted regions, reducing DOS after 72 h of aging and stabilizing Eb after 24 h at higher electrode potentials. However, the corrosion process is intensified at the σ/austenite interface, compromising intergranular corrosion resistance during prolonged aging. The findings show that complete recovery of corrosion resistance via self-healing is not achieved when high fractions of σ phase are formed. In addition, DICTRA calculations effectively evaluate corrosion resistance degradation from σ phase growth, providing deeper insights into the intergranular corrosion mechanism.
Implementing epic fast pass for echocardiogram and endoscopy to improve healthcare access and utilization
Access and efficient resource utilization remain critical challenges in healthcare, often leading to long wait times despite unfilled outpatient appointments. Epic Fast Pass (EFP), an innovative feature within the Electronic Health Record (EHR), has improved outpatient appointment scheduling, reduced wait times, and enhanced access in routine appointments. Guided by the principles of a learning health system, we describe the novel application of EFP to imaging and procedural services, specifically outpatient echocardiograms and endoscopies. We collected user data over 15 months for echocardiograms and 4 months for endoscopies. For echocardiograms, 41.26% of patients accepted an offer, improving their appointment times by an average of 12.8 days. For endoscopies, 48.35% accepted, with an average improvement of 50.43 days. Our results demonstrate that rescheduling tools for outpatient imaging and procedural appointments are both feasible and promising, with the potential to enhance patient access and optimize resource utilization.
Universal in situ supersaturated crystallization enables 3D printable afterglow hydrogel
Stretchable afterglow materials have garnered widespread attention owing to their unique combination of optical properties and mechanical flexibility. However, achieving a crystal environment to suppress the non-radiative transition of triplet excitons poses a challenge in constructing stretchable afterglow materials. Herein, we utilize an in situ supersaturated crystallization strategy to form afterglow microcrystals within a hydrogel matrix. This approach enables afterglow emission with a lifetime of 695 ms while maintaining high stretchability with tensile stress surpassing 398 kPa, extensibility over 400% and a high water content of 65.21%. Moreover, the universal supersaturated crystallization strategy allows for conferring tunable afterglow performance. Successful demonstrations in hydrogel 3D printing and anti-counterfeiting purposes showcase the potential for advanced applications of 3D printable afterglow hydrogels. This investigation provides guidelines for generally designing efficient afterglow hydrogels and addresses the inherent contradiction between flexibility and rigid in stretchable afterglow materials.
Focal cortical dysplasia (type II) detection with multi-modal MRI and a deep-learning framework
Focal cortical dysplasia type II (FCD-II) is a prominent cortical development malformation associated with drug-resistant epileptic seizures that leads to lifelong cognitive impairment. Efficient MRI, followed by its analysis (e.g., cortical abnormality distinction, precise localization assistance, etc.) plays a crucial role in the diagnosis and supervision (e.g., presurgery planning and postoperative care) of FCD-II. Involving machine learning techniques particularly, deep-learning (DL) approaches, could enable more effective analysis techniques. We performed a comprehensive study by choosing six different well-known DL models, three image planes (axial, coronal, and sagittal) of two MRI modalities (T1w and FLAIR), demographic characteristics (age and sex) and clinical characteristics (brain hemisphere and lobes) to identify a suitable DL model for analysing FCD-II. The outcomes show that the DenseNet201 model is more suitable because of its superior classification accuracy, high-precision, F1-score, and large area under the receiver operating characteristic (ROC) curve and precision–recall (PR) curve.
Patients’ perspective on the environmental impact of the severe dry eye disease healthcare pathway
The NHS has committed to achieving net-zero carbon emissions by 2045. Dry eye disease, a chronic condition affecting approximately 29.5% of the global population, poses a significant challenge due to its environmentally harmful care pathway, which also exacerbates the condition. This research article presents a multi-centre cross-sectional survey of patients with severe dry eye disease to examine the pollution and emissions associated with the NHS dry eye disease care pathway. The aim is to identify target areas where innovation can aid the NHS in reaching its net-zero goal.
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