Leveraging spatial data infrastructure for machine learning based building energy performance prediction. [PDF]
Sisman S, Kara A, Aydinoglu AC.
europepmc +1 more source
Improving the implementation of adaptive reuse strategies for historic buildings [PDF]
Conejos, Sheila +2 more
core
3D‐Printed Sulfur‐Derived Polymers With Controlled Architectures for Lithium‐Sulfur Batteries
Rheology‐guided formulation design for direct ink writing enables the fabrication of 3D sulfur copolymer cathodes with controlled architectures for lithium‐sulfur batteries. The printed electrodes exhibit multiscale porosity and high sulfur utilization, delivering enhanced electrochemical performance compared to conventional cast electrodes.
Bin Ling +7 more
wiley +1 more source
An option space approach to wood use: Providing structural timber for buildings while safeguarding forest integrity. [PDF]
Gingrich S +9 more
europepmc +1 more source
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz +3 more
wiley +1 more source
The multi-objective optimization of residential building glass in summer-hot and winter-cold regions using genetic algorithms: energy consumption, carbon emissions, and health performance analysis. [PDF]
He Y, Fu X, Li S, Guo J.
europepmc +1 more source
A food‐grade cooling composite made from starch and recycled eggshell powder offers a scalable, ultra‐low‐cost solution for passive daytime radiative cooling. Easily prepared using basic kitchen tools, this material empowers communities, even in areas with limited infrastructure, to stay cooler during worsening summer heat waves.
Qimeng Song +3 more
wiley +1 more source
A systematic review of the life cycle cost estimation of upgrading buildings for sustainability. [PDF]
Balasbaneh AT, Sher W, Li J.
europepmc +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source

