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Abstract The effective quality factor of the cantilever plays a fundamental role in dynamic mode atomic force microscopy. Here we present a technique to modify the quality factor of an atomic force microscopy cantilever within a Fabry–Perot optical interferometer. The experimental setup uses two separate laser sources to detect and excite the oscillation of the cantilever. While the intensity modulation of the excitation laser drives the oscillation of the cantilever, the average intensity can be used to modify the quality factor via optomechanical force without changing the fiber-cantilever cavity length. The technique enables users to optimize the quality factor for different types of measurements without influencing the deflection measurement sensitivity. An unexpected frequency shift was observed and modelled as temperature dependence of the cantilever’s Young’s modulus, which was validated using finite element simulation. The model was used to compensate for the thermal frequency shift. The simulation provided relations between optical power, temperature, and frequency shift. 

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers. 

Semiconductor moiré superlattices, characterized by their periodic spatial light emission, unveil a new paradigm of engineered photonic materials. Here, we show that ferroelectric moiré domains formed in a twisted hexagonal boron nitride (t-hBN) substrate can modulate light emission from an adjacent semiconductor MoSe₂monolayer. The electrostatic potential at the surface of the t-hBN substrate provides a simple way to confine excitons in the MoSe₂monolayer. The excitons confined within the domains and at the domain walls are spectrally separated because of a pronounced Stark shift. Moreover, the patterned light emission can be dynamically controlled by electrically gating the ferroelectric domains, introducing a functionality beyond other semiconductor moiré superlattices. Our findings chart an exciting pathway for integrating nanometer-scale moiré ferroelectric domains with various optically active functional layers, paving the way for advanced nanophotonics and metasurfaces. 

We report the growth and optical characterization of single-crystal BiFe1−xMnxO3 thin films directly on SrTiO3/Si(001) substrates using molecular beam epitaxy. X-ray diffraction confirmed epitaxial growth, film crystallinity, and sharp interface quality. Scanning electron microscopy and energy dispersive X-ray spectroscopy verified uniform film morphology and successful Mn incorporation. Spectroscopic ellipsometry revealed a systematic bandgap reduction with increasing Mn concentration, from 2.7 eV in BiFeO3 to 2.58 eV in BiFe0.74Mn0.26O3, consistent with previous reports on Mn-doped BiFeO3. These findings highlight the potential of BiFe1₋xMnxO3 films for bandgap engineering, advancing their integration into silicon-compatible multifunctional optoelectronic and photovoltaic applications. 

Acidic oxygen evolution reaction (OER) electrocatalysts that provide high activity, lower costs, and long-term stability are needed for the wide-scale adoption of proton-exchange membrane (PEM) water electrolyzers for generating hydrogen through electrochemical water splitting. We report the effects of chromium substitution and temperature treatments on the structure, OER activity, and electrochemical stability of ruthenium oxide (RuO2) aerogel OER electrocatalysts. RuO2 and Cr-substituted RuO2 aerogels (Ru0.6Cr0.4O2) were synthesized using sol–gel chemistry and then thermally treated at different temperatures. Introducing chromium into the synthesis increased the surface area (7–11 times higher) and pore volume (5–6 times higher) relative to RuO2 aerogels. X-ray diffraction analysis is consistent with s that Cr was substituted into the rutile RuO2 structure. X-ray photoelectron spectroscopy showed that trivalent Cr substitution altered the surface electronic structure and ratio of surface hydroxides. The specific capacitance values of Cr-substituted RuO2 aerogels were consistent with charge storage within a hydrous surface. Cr-substituted RuO2 aerogels exhibited 26 times the OER mass activity and 3.5 times the OER specific activity of RuO2 aerogels. Electrochemical stability tests show that Cr-substituted RuO2 aerogels exhibit similar stability to commercial RuO2. Understanding how metal substituents can be used to alter OER activity and stability furthers our ability to obtain highly active, durable, and lower-cost OER electrocatalysts for PEM electrolyzers.