Solar thermal fuels (STFs) are an unconventional paradigm for solar energy conversion and storage which is attracting renewed attention. In this concept, a material absorbs sunlight and stores the energy chemically via an induced structural change, which can later be reversed to release the energy as heat. An example is the azobenzene molecule which has a cis-trans photoisomerization with these properties, and can be tuned by chemical substitution and attachment to templates such as carbon nanotubes, small molecules, or polymers. By analogy to the Shockley-Queisser limit for photovoltaics, we analyze the maximum attainable efficiency for STFs from fundamental thermodynamic considerations. Microscopic reversibility provides a bound on the quantum yield of photoisomerization due to fluorescence, regardless of details of photochemistry. We emphasize the importance of analyzing the free energy, not just enthalpy, of the metastable molecules, and find an efficiency limit for conversion to stored chemical energy equal to the Shockley-Queisser limit. STF candidates from a recent high-throughput search are analyzed in light of the efficiency limit.
Organometallic Complexes of Graphene and Carbon Nanotubes: Introducing New Perspectives in Atomtronics, Spintronics, High Mobility Graphene Electronics and Energy Conversion Catalysis
Tailoring the Optoelectronic, Photocatalytic, Thermoelectric and Thermodynamic Properties of Halides Li2InBiX6 (X = Cl, Br, I) for Energy Conversion: A DFT Study
Princess Nourah Bint Abdulrahman University（公主诺拉·宾特·阿卜杜勒拉赫曼大学）；University of West Bohemia（西波希米亚大学）；University of Sargodha（萨尔戈达大学）；King Saud University（沙特国王大学）；Gachon University（迦南大学）
Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications
S.U. Umarov Physical-Technical Institute of NAST（纳兹尔科学院苏·乌马尔物理技术研究所）；School of Optoelectronic Engineering & CQUPT-BUL Innovation Institute, Chongqing University of Posts and Telecommunications（重庆邮电大学光电工程学院与CQUPT-BUL创新学院）
Relating Band Edge DOS Occupancy Statistics Associated Excited State Electrons Entropy Generation to Free Energy Loss and Intrinsic Voc Deficit of Solar Cells
该研究将带边 DOS 占据及激发态电子熵与太阳能电池自由能损失和本征 Voc 亏损关联，指出带边 DOS 工程是突破辐射复合损失、优化性能的新维度。
Proton-electron coupled catalyst for ionomer-free electrochemical energy conversion
University of California, Los Angeles（加州大学洛杉矶分校）；Georgia Institute of Technology（佐治亚理工学院）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Max Planck Institute for Dynamics of Complex Technical Systems（马克斯·普朗克复杂技术系统动力学研究所）；California NanoSystems Institute（加州纳米系统研究所）；California Institute of Technology（加州理工学院）
Catlas: an automated framework for catalyst discovery demonstrated for direct syngas conversion
在后化石燃料时代，催化剂的发现对于支持能源和关键化学原料的获取至关重要。使用从头算方法如密度泛函理论（DFT）对大的材料设计空间进行穷举计算搜索是不可行的。我们试图通过利用大型的、广义的、基于图的机器学习（ML）模型，以相对较低的计算成本探索大型设计空间，这些模型是预先训练的，因此不需要前期数据收集或训练。我们提出了catlas，一个框架，分布和自动生成的吸附物表面配置和ML推断的DFT能量，以实现这一目标。Catlas是开源的，使ML辅助催化剂筛选变得容易并可供所有人使用。为了证明其有效性，我们使用catlas来探索用于将合成气直接转化为多碳含氧化合物的催化剂候选物。对于这种情况下的研究，我们探索947稳定/亚稳二元，过渡金属间化合物作为可能的催化剂候选人。在这个子集的材料，我们能够预测的吸附能的关键描述符，*CO和 *OH，与近DFT精度（0.16，0.14 eV MAE，分别）。使用现有微动力学模型对C2+含氧化合物的预测选择性，我们确定了144种候选材料。对于10个有希望的候选人，DFT计算显示出与我们使用ML的评估有很好的相关性。最常见的元素组合是Pt-Ti、Pd-V、Ni-Nb和Ti-Zn，所有这些都是未经实验研究的。
