Researchers at Penn State developed a new class of 3D-printed bioelectronics made of soft, stretchy materials, as well as an adhesive component that helps the device painlessly stick to biological tissue. The team reported that their new design, which they call CaroFlex and tested in a rodent model, relieved hypertension while causing much less damage to surrounding tissue.

After the success of Artemis II, longer space journeys are expected, raising new health and nutritional challenges for astronauts. Current space foods rely on dried, shelf-stable items. A study published in ACS Food Science & Technology suggests fortified beverages could help fill nutrient gaps and add variety to astronauts’ diets. Researchers created drinks using emulsions that are stable in Earth’s gravity and microgravity. Their recipes deliver omega-3 fatty acids with customizable sweetness levels and flavors.

The practical applications of sulfide-based all-solid-state batteries (ASSBs) are hindered by poor interfacial compatibility between cathode active materials and sulfide solid electrolytes. A widely employed solution is to use a protective layer to prevent direct contact between the cathode materials and electrolyte. However, its minimum thickness has not been established. In this study, researchers determined that a 2.5 nm lithium niobium oxide protective layer is the minimum effective thickness for suppressing interfacial degradation in ASSBs.

A retrospective study of 17 drug-resistant epilepsy patients found that stereo-electroencephalography-guided radiofrequency thermocoagulation (RF-TC) rapidly altered brain network connectivity after treatment. Reductions in alpha-band connectivity within epileptogenic regions were linked to clinical outcomes, suggesting that short resting-state recordings may help predict treatment response. Findings support RF-TC as a network-modulating therapy rather than a purely local lesioning procedure and highlight its potential role in guiding more personalized epilepsy care.

Researchers have introduced a novel canonical correlation guided deep neural network (CCDNN) to improve multi-source data acquisition and fusion. The model is designed to integrate heterogeneous data more effectively, addressing limitations of existing fusion methods in handling diverse and high-dimensional inputs. By leveraging advanced neural network structures, CCDNN enhances feature representation and decision-making accuracy. Experimental results demonstrate superior performance across benchmark tasks, highlighting its potential for applications in intelligent control, automation, and data-driven engineering systems.

New research led by the University of Oxford and University College London (UCL) has revealed that pollution from coal-fired power plants is significantly reducing the energy output of solar photovoltaic (solar PV) installations, particularly where these are expanding side by side. The findings have been published today (15 May) in Nature Sustainability.