Search

Structural color filters have recently blossomed as a superior alternative to organic dyes or chemical pigments owing to their remarkable durability and compactness. With appropriate design, nanostructure‐induced photonic or plasmonic resonance modes can give rise to either enhancement in transmission or suppression in the reflection within specific wavelength ranges in the optical regime, generating distinctive colors. However, the static optical properties due to fixed structural geometry and size after fabrication hinder their deployment in many cutting‐edge technologies requiring adaptive complexion changes. Here, a multilayer thin film‐based color filter incorporating Mg and MgO, earth‐abundant and biodegradable materials, is devised. The devices display vivid hues spanning a broad gamut via the control of the film thickness. They also exhibit minimal color changes with varying angle views up to 40°–50°. Moreover, the tones fade away instantly upon immersion in water and then progressively transition to a different hue with the complete removal of the Mg‐containing layers, realizing transient color responses. This approach holds great promise for alternative pixels with irreversible color‐change capability as well as zero‐power consumption and low cost, while making use of biodegradable materials. [ABSTRACT FROM AUTHOR]

Metal halide perovskites (MHP) can be made more stable through the addition of small amounts of cesium. Despite the improvement, these multication absorbers still display strong environmental sensitivity to any combination of factors, including water, oxygen, bias, temperature, and light. Here, the relationship is elucidated between light absorption, charge carrier radiative recombination, and relative humidity (rH) for the Cs0.05FA0.79MA0.16Pb(I0.83Br0.17)3 composition, revealing partially reversible reductions in the extinction coefficient and fully reversible 25× enhancements in absolute light emission registered across the same humidity cycles up to 70% rH. With in situ excitation wavelength‐dependent measurements, irreversible changes are identified in the perovskite after a single cycle of humidity‐dependent photoluminescence (PL) performed with 450 nm excitation. The in situ measurement platform can be extended to test the effect of other stressors on thin films' optical behavior. [ABSTRACT FROM AUTHOR]

Many technological applications in photonics require devices to function reliably under extreme conditions, including high temperatures. To this end, materials and structures with thermally stable optical properties are indispensable. State-of-the-art thermal photonic devices based on nanostructures suffer from severe surface diffusion-induced degradation, and the operational temperatures are often restricted. Here, we report on a thermo-optically stable superabsorber composed of bilayer refractory dielectric materials. The device features an average absorptivity ∼95% over >500 nm bandwidth in the near-infrared regime, with minimal temperature dependence up to 1500 °C. Our results demonstrate an alternative pathway to achieve high-temperature thermo-optically stable photonic devices., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

The composition-dependent degradation of hybrid organic-inorganic perovskites (HOIPs) due to environmental stressors still precludes their commercialization. It is very difficult to quantify their behavior upon exposure to each stressor by exclusively using trial-and-error methods due to the high-dimensional parameter space involved. We implement machine learning (ML) models using high-throughput, in situ photoluminescence (PL) to predict the response of Cs y FA 1- y Pb(Br x I 1- x ) 3 while exposed to relative humidity cycles. We quantitatively compare three ML models while generating forecasts of environment-dependent PL responses: linear regression, echo state network, and seasonal autoregressive integrated moving average with exogenous regressor algorithms. We achieve accuracy of >90% for the latter, while tracking PL changes over a 50 h window. Samples with 17% of Cs content consistently showed a PL increase as a function of cycle. Our precise time-series forecasts can be extended to other HOIP families, illustrating the potential of data-centric approaches to accelerate material development for clean-energy devices., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Optical materials based on unconventional plasmonic metals (e.g., magnesium) have lately driven rising research interest for the quest of possibilities in nanophotonic applications. Several favorable attributes of Mg, such as earth abundancy, lightweight, biocompatibility/biodegradability, and its active reactions with water or hydrogen, have underpinned its emergence as an alternative nanophotonic material. Here, we experimentally demonstrate a thin film-based optical device composed exclusively of earth-abundant and complementary metal-oxide semiconductor (CMOS)-compatible materials (i.e., Mg, a-Si, and SiO 2 ). The devices can exhibit a spectrally selective and tunable near-unity resonant absorption with an ultrathin a-Si absorbing layer due to the strong interference effect in this high-index and lossy film. Alternatively, they can generate diverse reflective colors by appropriate tuning of the a-Si and SiO 2 layer thicknesses, including all the primary colors for RGB (red, green, blue) and CMY (cyan, magenta, yellow) color spaces. In addition, the reflective hues of the devices can be notably altered in a zero power-consumption fashion by immersing them in water due to the resulted dissolution of the Mg back-reflection layer. These compelling features in combination with the lithography-free and scalable fabrication steps may promise their adoption in various photonic applications including solar energy harvesting, optical information security, optical modulation, and filtering as well as structure reuse and recycling., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

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.