ABSTRACT
Perovskite solar cells (PSC) are a favorable candidate for next-generation solar systems with efficiencies comparable to Si photovoltaics, but their long-term stability must be proven prior to commercialization. However, traditional trial-and-error approaches to PSC screening, development, and stability testing are slow and labor-intensive. In this Perspective, we present a survey of how machine learning (ML) and autonomous experimentation provide additional toolkits to gain physical understanding while accelerating practical device advancement. We propose a roadmap for applying ML to PSC research at all stages of design (compositional selection, perovskite material synthesis and testing, and full device evaluation). We also provide an overview of relevant concepts and baseline models that apply ML to diverse materials problems, demonstrating its broad relevance while highlighting promising research directions and associated challenges. Finally, we discuss our outlook for an integrated pipeline that encompasses all design stages and presents a path to commercialization.
ABSTRACT
Metallic materials with engineered optical properties have the potential to enhance the performance of energy harvesting and storage devices operating at the macro- and nanoscale, such as solar cells, photocatalysts, water splitting, and hydrogen storage systems. For both thin films and subwavelength nanostructures, upon illumination, the coherent oscillation of charge carriers at the interface with a dielectric material gives rise to resonances named surface plasmon polariton (SPP) and localized surface plasmon resonance (LSPR), respectively. These resonances result in unique light absorption, scattering, and transmission responses over the electromagnetic spectrum, which, in turn, can be exploited to tailor the behavior of active metallic components in optoelectronic devices containing Ag, Au, Cu, Al, Mg, among other metals. The wavelength in which the resonances occur primarily depends on the metal itself (i.e., the dielectric function or permittivity), the dielectric medium surrounding the metals, and the size, geometry, and periodicity of the metallic nanostructures. Nevertheless, the aforementioned parameters allow a limited modulation of both SPP and LSPR over a narrow window of frequencies. To overcome this constraint, we have proposed and realized the alloying of metals via physical deposition methods as a paradigm to almost arbitrarily tuning their optical behavior in the UV-NIR, which leads to permittivity values currently not available. Our approach offers an additional knob, chemical composition, to engineer light-matter interactions in metallic materials. This Account highlights recent progress in using alloying as a pathway to control the optical behavior of metallic thin films and nanostructures for energy harvesting and storage applications, including (photo)catalysis, photovoltaics, superabsorbers, hydrogen storage, among other systems. We choose to primarily focus on the optical properties of the metallic mixtures and in their near- to far-field responses in the UV-NIR range of the spectrum as they represent key parameters for materials' selection for the devices above. By alloying, it is possible to obtain metallic materials with LSPR not available for pure metals, which can enable the further control of the electromagnetic spectrum. First, we discuss how the permittivity of binary mixtures of coinage metals (Au, Ag, and Cu) can be tailored based on the chemical composition of their pure counterparts. Second, we present how novel metallic materials can be designed through band structure engineering through density functional theory (DFT), a paradigm that could benefit from artificial intelligence methods. Concerning alloyed thin films, we discuss the promise of earth-abundant metals and provide an example of the superior performance of AlCu in superabsorbers. In the realm of nanostructures, we focus the discussion on physical deposition methods, where we provide a detailed analysis of how chemical composition can affect the far- and near-field responses of metallic building blocks. Finally, we provide a brief outlook of promising next steps in the field.
ABSTRACT
Sensor miniaturization together with broadening temperature sensing range are fundamental challenges in nanothermometry. By exploiting a large temperature-dependent screening effect observed in a resonant tunneling diode in sequence with a GaInNAs/GaAs quantum well, we present a low dimensional, wide range, and high sensitive nanothermometer. This sensor shows a large threshold voltage shift of the bistable switching of more than 4.5 V for a temperature raise from 4.5 to 295 K, with a linear voltage-temperature response of 19.2 mV K(-1), and a temperature uncertainty in the millikelvin (mK) range. Also, when we monitor the electroluminescence emission spectrum, an optical read-out control of the thermometer is provided. The combination of electrical and optical read-outs together with the sensor architecture excel the device as a thermometer with the capability of noninvasive temperature sensing, high local resolution, and sensitivity.
