Gallium oxide (Ga₂O₃) is a semiconductor material that could make electronic devices much more energy-efficient than current silicon-based technology. Electronic diodes require two types of semiconductor layers to function properly, negative-type (n-type) and positive-type (p-type) layers. Scientists could reliably produce n-type gallium oxide layers but struggled to create stable p-type layers because gallium oxide's crystal structure naturally resists the atoms needed for these layers. This limitation resulted in gallium oxide semiconductors with poor performance and reliability issues. Now, researchers at Nagoya University in Japan have solved this manufacturing challenge and created the first functional pn diodes using gallium oxide. Their method, published in the Journal of Applied Physics, enables the use of gallium oxide for improved semiconductors and energy efficient devices. In addition, these new pn diodes can carry twice as much electrical current as previous gallium oxide diodes. 

A research team has demonstrated that three-dimensional (3D) imaging can provide accurate, high-throughput measurements of phyllotaxy—the arrangement of leaves on a plant—across diverse sorghum genotypes.

A research team has developed a novel weakly supervised deep learning method that reconstructs spectral data from inexpensive RGB images, eliminating the need for manual labeling.

Journal of Bioresources and Bioproducts reports a sodium-alginate aerogel doped with 30% phosphorylated chitosan that records LOI 33.7%, PHRR 44 % drop and 0.035 W m⁻¹ K⁻¹ conductivity, promising halogen-free insulation.

Journal of Bioresources and Bioproducts reports an amino-functionalised cellulose hydrogel that harvests motion energy at 732 mW m⁻², retains 97 % output after 24 000 bends, and drives wireless handwriting and fatigue-alert systems without external power.

In the Journal of Bioresources and Bioproducts, researchers unveil a coaxial wet-spun solid-solid phase-change fiber that pairs a cellulose sheath with a polyurethane–hexamethylene-diisocyanate sponge core. The hierarchically porous structure yields high latent heat, mechanical strength and zero leakage, extending thermal-response time by up to 12 minutes compared with PET textiles.

Identifying embryos with the highest likelihood of successful implantation is a critical component of the in vitro fertilization (IVF) process. Visual assessments are limited by the subjectivity of embryologists, making consistent evaluation of embryo health challenging with traditional methods. Recent advances in artificial intelligence (AI)—particularly in computer vision and deep learning—have enabled the automated analysis of embryo morphology images, reducing subjectivity and improving evaluation efficiency. Through an extensive literature search using keywords such as “embryo health assessment” and “artificial intelligence,” the present review focuses on AI-driven approaches for automated embryo evaluation. It examines AI techniques applied to embryo assessment across the early development, blastocyst, and full developmental stages. This review indicated the promising potential of AI technologies in enhancing the precision, consistency, and speed of embryo selection. AI models have been reported to outperform manual evaluations across several parameters, offering promising opportunities to improve success rates and operational efficiency in reproductive medicine. Additionally, this review discusses the current limitations of AI implementation in clinical settings and explores future research directions. Overall, the review provides insight into AI’s growing role in advancing embryo selection and highlights the path toward fully automated evaluation systems in assisted reproductive technology.