Controlling the Functionality of Materials for Sustainable Energy
Abstract
Our understanding and control of sustainable energy technologies is in its infancy. Many sustainable energy phenomena depend on the exchange of photons and electrons among quantized energy levels of semiconductors, molecules, and metals at nanoscale spatial scales and at fast or ultrafast time scales. Improving the performance of sustainable energy technologies to make them competitive with fossil technologies requires probing and understanding these quantum phenomena with advanced scientific techniques. This understanding must then be translated into control of the functionality and performance of the materials and chemistry that govern sustainable energy technologies. The review begins by contrasting the foundations of fossil fuel technology based on combustion, heat, and classical thermodynamics with the foundations of sustainable energy technology based on quantum exchange of energy among photons, chemical bonds, and electrons without conversion to heat. Two sets of tools that are essential to observe, understand, and control the quantum phenomena of sustainable energy are described: in situ and time-resolved experiments and theory, and numerical modeling of the functionality of large assemblies of atoms. Finally, the challenges and opportunities for understanding and ultimately controlling sustainable energy phenomena are presented for catalysis, solar water splitting, and superconductivity.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.