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Nanoscale porous coordination polymers were synthesized using simple wet chemical method. The effect of various polymer surfactants on colloidal stability and shape selectivity was investigated. Our results suggest that the nanoparticles exhibited significantly improved adsorption kinetics compared to bulk crystals due to decreased diffusion path lengths and preferred crystal plane interaction.
The practicality and economic viability of hybrid perovskite solar cells hinge on their operational lifetime, and methods for forecasting the performance of perovskites under different operational stresses are urgently needed. Here, we explore the evolution of material-level optoelectronic properties as MAPbI 3 degrades and discover universal behaviors where the carrier diffusion length (L D ) decays before quasi-Fermi-level splitting (ΔE F ), regardless of the specific stress protocol (oxygen, humidity, thermal stress, or a combination). We employ a machine learning greedy feature selection model that uses initially measured properties to predict the time it takes L D to decrease to 85% of its initial value with a prediction accuracy of 12.8%. This model reveals a strong correlation between the initial rate of transmittance change and the time until loss of transport. We translate this material-level finding to photovoltaic device-level forecasting by demonstrating that the rate of change of transmittance is equivalent to the rate of change of the spatial standard deviation of dark-field image intensity (i.e., scattered light intensity) collected in reflection mode (and thus applicable to devices with opaque contacts). Furthermore, this work demonstrates that transmittance and scattering methods are highly effective for accelerated material (and device) stability evaluation and forecasting.
Garnet-based solid-state electrolytes (SSEs) represent a promising class of materials for next-generation batteries with improved safety and performance. However, lack of control over the composition and crystal structure of the well-known Li 7 La 3 Zr 2 O 12 (LLZO) garnet material has led to poor reproducibility with a wide range of ionic conductivities reported in the literature. In this study, the role of precursor homogeneity in controlling the compositional and structural evolution of Al-doped LLZO is explored. A novel solution-based synthesis approach is employed to demonstrate enhanced atomic-scale mixing of the starting materials in comparison to conventional solid-state preparation methods. Through this technique, it is shown that the stability and formation temperature of the highly conductive cubic phase is directly impacted by the spatial distribution of the doping element and reactant species in the precursor mixture. Precursor homogeneity was also an important factor in mitigating the formation of unwanted secondary impurities. These findings can be used to guide the synthesis of SSEs with reproducible material characteristics and enhanced electrolytic performance.
During gravity measurements with Raman type atom interferometry, the frequency of the laser used to drive Raman transition is scanned by chirping the frequency of a direct digital synthesizer (DDS), and the local gravity is determined by precisely measuring the chip rate α of DDS. We present an effective method that can directly evaluate the frequency chirp rate stability of our DDS. By mixing a pair of synchronous linear sweeping signals, the chirp rate fluctuation is precisely measured with a frequency counter. The measurement result shows that the relative α instability can reach 5.7 × 10{sup −11} in 1 s, which is neglectable in a 10{sup −9} g level atom interferometry gravimeter.
In this paper we present and analyze a general framework for constructing high order explicit local time stepping (LTS) methods for hyperbolic conservation laws. In particular, we consider the model problem discretized by Runge-Kutta discontinuous Galerkin (RK-DG) methods and design LTS algorithms based on the strong stability preserving Runge-Kutta (SSP-RK) schemes, that allow spatially variable time step sizes to be used for time integration in different regions of the computational domain. The proposed algorithms are of predictor-corrector type, in which the interface information along the time direction is first predicted based on the SSP-RK approximations and Taylor expansions, and then the fluxes over the region of the interface are corrected to conserve mass exactly at each time step. Following the proposed framework, we detail the corresponding LTS schemes with accuracy up to the fourth order, and prove their conservation property and nonlinear stability for the scalar conservation laws. Numerical experiments are also presented to demonstrate excellent performance of the proposed LTS algorithms.
Choosing which properties of the data to use as input to multivariate decision algorithms—also known as feature selection—is an important step in solving any problem with machine learning. While there is a clear trend towards training sophisticated deep networks on large numbers of relatively unprocessed inputs (so-called automated feature engineering), for many tasks in physics, sets of theoretically well-motivated and well-understood features already exist. Working with such features can bring many benefits, including greater interpretability, reduced training and run time, and enhanced stability and robustness. We develop a new feature selection method based on distance correlation, and demonstrate its effectiveness on the tasks of boosted top- and W -tagging. Using our method to select features from a set of over 7,000 energy flow polynomials, we show that we can match the performance of much deeper architectures, by using only ten features and two orders-of-magnitude fewer model parameters. Published by the American Physical Society 2024
We argue that all locality-preserving mappings between fermionic observables and Pauli matrices on a two-dimensional lattice can be generated from the exact bosonization in Chen et al., whose gauge constraints project onto the subspace of the toric code with emergent fermions. Starting from the exact bosonization and applying Clifford finite-depth generalized local unitary transformation, we can achieve all possible fermion-to-qubit mappings (up to the re-pairing of Majorana fermions). In particular, we discover a new supercompact encoding using 1.25 qubits per fermion on the square lattice. We prove the existence of finite-depth quantum circuits to obtain fermion-to-qubit mappings with qubit-fermion ratios r = 1 + 1/2k for positive integers k, utilizing the trivialness of quantum cellular automata in two spatial dimensions. Also, we provide direct constructions of fermion-to-qubit mappings with ratios arbitrarily close to 1. When the ratio reaches 1, the fermion-to-qubit mapping reduces to the one-dimensional Jordan-Wigner transformation along a certain path in the two-dimensional lattice. Finally, we explicitly demonstrate that the Bravyi-Kitaev superfast simulation, the Verstraete-Cirac auxiliary method, Kitaev’s exactly solved model, the Majorana loop stabilizer codes, and the compact fermion-to-qubit mapping can all be obtained from the exact bosonization.
The universal droop control (UDC) can be applied to power inverters having an impedance angle between -pi/2 rad and pi/2 rad to achieve voltage and frequency regulation and accurate proportional power sharing without the need of knowing the type or value of impedance. However, there is an increasing need for voltage and frequency regulation within predetermined ranges even under some abnormal conditions, such as overloading, sensor faults, and large set-point changes, etc. In this paper, a bounded nonlinear dynamics is introduced into the UDC to ensure that the voltage and frequency can always stay within predetermined ranges under normal and some abnormal conditions, up to hardware limits when current protection needs to be triggered. As a result, the proposed controller can extend the operational range of power inverters in terms of voltage and frequency regulation to cover different scenarios. More importantly, the ranges for both voltage and frequency can be chosen independently from each other. Since the original structure of the UDC is kept in the proposed controller, the properties of the UDC, such as without the need of knowing the type or value of impedance, are well maintained. Furthermore, the closed-loop stability of the system is established via the Lyapunov method. Extensive simulation and experimental results are presented to validate the effectiveness of the proposed controller.
This paper presents the results of an experimental investigation of the parametric stabilization of Rayleigh-Bénard convection through the imposition of sinusoidal vibration. The ability to dynamically stabilize Rayleigh-Bénard convection using acceleration modulation is of interest to groups who design and study thermoacoustic machines as the introduction of parasitic convection can have deleterious effects on the desired operation and thermodynamic efficiency of the device. These performance issues caused by suspected convective instability have been seen both in traveling wave thermoacoustic refrigerators and cryogenic pulse tube chillers. This paper reports the results of an experiment intended to determine the vibratory, fluidic, and geometric conditions under which a small, rectangular container of statically unstable fluid may be stabilized by vertical vibration with comparison to the computational methods of R. M. Carbo [J. Acoust. Soc. Am. 135, 654–668 (2014)]. Measurements are obtained using a large-displacement kinematic shaker of an original design with the convecting gas characterized using both thermal transport measurements and flow visualization employing tracer particles illuminated by a diode laser light sheet phase-locked to the shaker. These experiments are believed to be the first demonstrating the suppression of convection through vibration in rectangular containers.
For modern microelectronics, at the present time, the technologies of consciousness smart structures play an important role, which can provide accuracy, stability and high quality of the structures. Submicron lithography methods are quite expensive and have natural size limitations, not allowing the production of structures with an extremely small lateral limitation. Therefore, an intensive search was conducted for alternative methods for creating submicron resolution structures. Especially attractive one is the possibility of self-organization effects utilization, where the nanostructure of a certain size is formed under the influence of internal forces. The dip pen nanolithography method based on a scanning probe microscope uses a directwrite technology and allows one to carry out a playback of small size structures with high accuracy. In the experiment, a substrate coated with Au (15 nm) using a DPN technique is applied to the polymer to form a desired pattern nano-sized channel. The experiment was conducted using a pointed probe SiN, coated MHA-Acetonitrile, on the Si(111)/Fe{sub 3}Si/Au structure.
Deep learning (DL), in particular deep neural networks, by default is purely data-driven and in general does not require physics. This is the strength of DL but also one of its key limitations when applied to science and engineering problems in which underlying physical properties—such as stability, conservation, and positivity—and accuracy are required. DL methods in their original forms are often not capable of respecting the underlying mathematical models or achieving desired accuracy even in big-data regimes. On the other hand, many data-driven science and engineering problems, such as inverse problems, typically have limited experimental or observational data, and DL would overfit the data in this case. Leveraging information encoded in the underlying mathematical models, we argue, not only compensates for missing information in low data regimes but also provides opportunities to equip DL methods with the underlying physics, hence promoting better generalization. This paper develops a model-constrained DL approach and its variant TNet—a Tikhonov neural network—which are capable of learning not only information hidden in the training data but also in the underlying mathematical models to solve inverse problems governed by partial differential equations in low data regimes. We provide the constructions and some theoretical results for the proposed approaches for both linear and nonlinear inverse problems. Since TNet is designed to learn inverse solutions with Tikhonov regularization, it is interpretable: in fact it recovers Tikhonov solutions for linear cases while potentially approximating Tikhonov solutions for nonlinear inverse problems. We also prove that data randomization can enhance not only the smoothness of the networks but also their generalizations. Comprehensive numerical results confirm the theoretical findings and show that with even as little as 1 training data sample for one-dimensional (1D) deconvolution, 5 for an inverse 2D heat conductivity problem, 100 for inverse initial conditions for a time-dependent 2D Burgers’s equation, and 50 for inverse initial conditions for 2D Navier–Stokes equations, TNet solutions can be as accurate as Tikhonov solutions while being several orders of magnitude faster. Furthermore, this is possible owing to the model-constrained term, replications, and randomization.
The interactions of two propagating pits on a single cathode surface were evaluated across variations in chloride concentration, water layer (WL), pit sizes, separation distance (x 2 ), and cathode size (L Cath ) under freely corroding conditions using Finite Element Methods (FEM). Calculated FEM current was utilized to predict stability based on the Galvele pit stability product. FEM predictions were utilized to train a neural network machine learning model for rapid stability predictions. Pit one is in the center of a circular cathode while pit two moves radially from the center pit. With two pits, the overall current in each pit is decreased with respect to a single pit, however, the total current is increased. Increasing WL and L Cath generally increased overall current in each pit and increased predicted maximum pit sizes. Increasing x 2 decreased current in pit two due to less cathode being available to support dissolution in proximity to pit two. Increasing chloride concentration from 0.6 to 3 M NaCl increased current, while increasing from 3 to 5.3 M NaCl decreased current. An overall increase in predicted pit size with increase in chloride concentration is predicted. A machine learning model was created to predict current and maximum pit size and captured underlying physics and predicted stability across the multidimensional parameter space.
A research-focused Python package that implements our hallucination-detection method for large language models(LLMs). The code computes stability signals from LLM predictions across various hyperparameters sweeps and combines free-energy/entropy–style metrics to flag likely hallucinations, with tunable thresholds for batch scoring. The repo includes evaluation scripts, config files, and example notebooks to reproduce benchmark results and ablations; it depends on standard open-source libraries (PyTorch, Hugging Face) and runs on CPU/GPU. The repository examples only use public models/datasets only.
We present results of evolutionary simulations based on density functional calculations of a potentially new type of energetic materials called extended solids: P-N and N-H. High-density structures with covalent bonds generated using variable and fixed concentration methods were analysed in terms of thermo-dynamical stability and agreement with experimental X-ray diffraction (XRD) spectra. X-ray diffraction spectra were calculated using a virtual diffraction algorithm that computes kinematic diffraction intensity in three-dimensional reciprocal space before being reduced to a two-theta line profile. Calculated XRD patterns were used to search for the structure of extended solids present at experimental pressures by optimizing data according to experimental XRD peak position, peak intensity and theoretically calculated enthalpy. Elastic constants has been calculated for thermodynamically stable structures of P-N system.
Reliability analysis is used to evaluate the probability of failure of a flood defence embankment subject to a blanket layer foundation condition, where high seepage forces increase the likelihood of internal erosion and slope failure. The stochastic effect of hydraulic conductivity and blanket layer thickness, in addition to soil shear strength properties, is considered for the underseepage and slope stability failure modes using the first-order reliability method. The blanket layer thickness controls the factor of safety (F) and reliability, followed by the unit weight of the blanket layer. Despite variation over orders of magnitude, hydraulic conductivity is less important than and comparable to the effect of soil shear strength parameters. Aleatory uncertainty is evaluated using fragility curves and a confidence interval on the expected value of F (stochastic F). Uncertainty in probability distribution parameters allows quantification of a subset of epistemic uncertainty and is used to construct confidence intervals for fragility.
This review article focuses on correlating the catalytic reactivity of NPs and their geometry. It illustrated that chemisorption and catalytic properties such as the onset reaction temperature, the activity, and selectivity of a nanocatalyst may be tuned through controlled synthesis of NPs with well-defined sizes and shapes. It has become clear that detailed information on the physical/chemical properties of supported NP systems must be available for the rational design of the next generation of nanosized catalysts. Such NP systems must be representative of active catalysts, and must be sufficiently well-defined ( e.g., with respect to size and shape) to serve as a basis for theoretical modeling. In addition, a synergistic combination of in situ and in operando characterization techniques working under realistic reaction conditions ( e.g. elevated temperatures and high pressure) must be available, since the working state of a NP catalyst might not be the state in which the catalyst was prepared, but a structural and/or chemical isomer that adapted to the particular reaction conditions. In order to address the complexity of real-world catalysts, a synergistic approach taking advantage of a variety of state-of-the-art experimental and theoretical methods must be undertaken. In this review, we have described some of the impressive developments that have been made in these areas. Many serious challenges still remain in the areas of catalyst characterization and design. One such challenge is to design highly active metal NP catalysts using abundant, inexpensive metals which can replace precious metal catalysts. Additionally, there is an increasingly pressing need for catalyst development for energy production applications, such as in fuel cells or for the synthesis of chemicals and fuels. Further work is needed to design more active and selective catalysts for these reactions that are viable at an industrial scale. In the coming years, nanocatalysis research must meet these challenges by building on the work described in this report. Further advancements in developing new synthetic methods will allow increased control over NP structure and stability, allowing for new insights into NP structure-reactivity correlations. In addition, the development of higher resolution in situ and in operando measurement techniques capable of probing catalytic reactions at NP surfaces over an extended range of temperatures and pressures and harsh environmental conditions will give unprecedented insight into NP catalysts in their working state. Single-nanoparticle spectroscopic and catalytic characterization must also be further developed. Advancements in computational modeling of more realistic catalysts will also bring us closer to gaining a molecularlevel view of a reaction at a NP surface when used to complement experimental studies. Such progress will bring us closer to realizing the goal of synthesizing highly effective, tailor-made, tunable nanoparticle catalysts.
The objective of the project is to optimize the deposition and post-deposition annealing conditions of quantum-cutting materials to enable coatings with high photoluminescence quantum yields and optimal absorption onset. These coatings will be integrated with commercial solar photovoltaic cells to enable improved power conversion efficiency, and the quantum-cutting cell stability will be ensured using industry-standard packaging methods.
NSLS-II accelerator physicists are working on scientific support of the NSLS-II operations and start-up of the accelerator systems including Storage Ring, Injector, beam diagnostics, and top-off injection; improvement of beam stability and orbit feedbacks; commissioning of a superconducting wiggler for the HEX beamline; development of a new operation mode for time-resolved experiments. The routine lattice characterization is continued to keep tracking the machine, the storage ring lattice model including strong coupling is developed, and seasonal variations of the ring circumference are studied. The collaboration with the EIC project is ongoing. In the frameworks of three LDRD projects, the physicists are working on the lattice development, studies of collective effects, and specifications of alignment and stability for the NSLS-II accelerator upgrade; novel methods of lattice design and optimization; studies of free-electron laser options; ultrafast electron diffraction and microscopy. Research accomplishments achieved in 2022, are summarized in this report.