The most notable scientific milestone in photovoltaics in the past several years is the emergence of solar cells based on hybrid organic-inorganic perovskite materials. While conventional silicon and thin-film solar cells have seen steady improvements in their power-conversion efficiencies (PCEs) spanning several decades, hybrid perovskite solar cells have already reached a certified 22.1% PCE (1), matching conventional solar cell technologies in only a few years since their first device architecture was tested. Setting the stage for a disruptive technology in the field of photovoltaics is the seemingly winning combination of properties of hybrid perovskite materials: high absorption coefficient and a tunable energy band gap in wavelengths ideal for solar cells; long diffusion lengths and lifetimes for photogenerated charge carriers, which easily dissociate into efficiently collected electrons and holes; Earth-abundant elemental composition; and their compatibility with low-cost and low-temperature fabrication methods (2–5). The results add, literally, a new dimension to the further development of high-performance perovskite solar cells.
Over the past five years, rapid progress in photovoltaic technology has been further accelerated by materials called perovskites. They require only common ingredients and relatively easy manufacturing methods, holding out the possibility of cheap thin-film cells on a variety of surfaces or combined with silicon in large panels. In the laboratory, small-area cells made with these materials already feature solar-conversion efficiencies as high as 22%, rivaling those of traditional silicon solar cells.
Machine learning's ability to perform intellectually demanding tasks across various fields, materials science included, has caused it to receive considerable attention. Many believe that it could be used to unlock major time and cost savings in the development of new materials. The growing demand for the use of machine learning to derive fast-to-evaluate surrogate models of material properties has prompted scientists at the National Institute for Materials Science in Tsukuba, Japan, to demonstrate that it could be the key driver of the "next frontier" of materials science in recently published research. To learn, machines rely on processing data using both supervised and unsupervised learning. With no data, however, there is nothing to learn from.
Lithium-sulfur (Li-S) batteries have been pursued as an alternative to lithium-ion (Li-ion) batteries for powering electric vehicles due to their ability to hold up to four times as much energy per unit mass as Li-ion. However, Li-S batteries don't come without some problems. For instance, the sulfur in the electrode can become depleted after just a few charge-discharge cycles, or polysulfides can pass through the cathode and foul the electrolyte. Another issue Li-S batteries face is the difficulty of ensuring that they operate safely at high temperatures due to their low boiling and flash temperatures. Now, researchers at the University of Western Ontario, in collaboration with a team from the Canadian Light Source, have leveraged a relatively new coating technique dubbed molecular layer deposition (MLD) that promises to lead to safe and durable high-temperature Li-S batteries.
The working principles of semiconductor devices are crucially determined by their band gap--the amount of energy needed to excite immobile charge carriers into ones that conduct current. In many inorganic semiconductors, band gaps can be tuned in a systematic way by alloying or inducing strain in the material. Although devices based on organic semiconductors are already in commercial use, there are few rational approaches for similar "band structure engineering" of these materials. On page 1446 of this issue, Schwarze et al. (1) now demonstrate, using long-range Coulomb interactions, a tuning effect of the band structure for organic semiconductors that are weakly bound by van der Waals forces. This effect, which has been totally neglected in discussions of the electronic states, is rather closely related to universal features of organic molecular crystals (2).