The performance of batteries and the associated operating areas depend, among other things, on the 3D microstructures of the electrode materials, and thus fundamental research is required in the field of electrode design. A multiscale microstructure-resolved 3D model is developed that investigates two different LiFePO4 freeze-casted electrode structures, that is, cellular and lamellar. The microstructure is simulated directly from the X-ray computed tomography data and the nanostructure is combined with the pseudo-2D simulation approach, where the morphological parameters and the distribution of the binder, carbon, and LiFePO4 are obtained from ex situ focused ion beam scanning electron microscopy measurements. The discharge performance shows that the lamellar structure exhibits a lower ohmic overvoltage and achieves a higher gravimetric capacity compared to the cellular structure, even though both electrode materials have the same porosity and amount of active material. The simulation reveals that the performance is not only directly influenced by the lithium-ion transport through the porous structure but also by the current distribution through the active material. Based on these insights, lamellar electrode structures should be considered for next-generation battery electrodes. The modeling approach can assist in electrode fabrication by identifying defects or suggesting better structural parameters.

New algorithms for the optimization of alloy nanoparticles (nanoalloys) are presented. The new algorithms are based on the concept of multiple basin-hopping walkers running in parallel, each with its own specialized task—the flying walker, exploring the energy landscape at high temperatures to sample different geometric structures, and the landing and hiking walkers mainly refining the optimization of chemical ordering at low temperatures. These algorithms are referred to as flying-landing (FL) and flying-landing-hiking (FLH). The algorithms are tested against several benchmarks (AuCu and AuRh clusters of 400 atoms and PtNi clusters of 38 and 55 atoms). In all cases, both FL and FLH are shown to perform very well compared to previous results in the literature. In addition, the algorithms are applied to the optimization of larger AgCu nanoparticles, with sizes up to 4000 atoms, in order to establish the behavior of the mixing energy and to compare full global optimization of shape and chemical ordering with optimization of chemical ordering alone at a fixed shape. In general, the results show that the simultaneous optimization of shape and chemical ordering is necessary in many cases, and that the FLH approach is especially efficient for that purpose.

The spectacular physical phenomenon of surface plasmon resonance (SPR) is the essence of present-day plasmonic sensors. Meanwhile, the unique properties of the interaction between light and matter have been carved out into the development of modern-day diagnostic biosensors. Plasmons, in simple terms, are oscillating free electrons in metallic nano-structures triggered by an incoming electromagnetic (EM) wave. With the advantages of real-time and label-free bio-sensing, plasmonic sensors are being utilized in multiple diverse areas of food technology, the bio-medical diagnostic sector, and even the chemical industry. Although this review will be brief, readers can gain a comprehensive picture of the essential elements by taking a broader look into the exploration of SPR sensor design via simulated studies and representative experimental plasmonic schemes developed for bio-sensing. In short, the various SPR sensing schemes that researchers have explored to realize enhanced SPR sensitivity are reviewed and summarized. Different experimental plasmonic sensors are also examined in which new SPR excitation schemes have been adopted. These "unconventional" designs, specifically those involving hybrid localized surface plasmon resonance (LSPR)-SPR excitation, may inspire those in the plasmonic field.

Barium zirconium sulfide (BaZrS3), a chalcogenide perovskite, is attracting a lot of attention for thin-film photovoltaic (PV) application. Unlike the lead halide perovskites, it is stable and does not contain toxic elements. Herein, PV devices incorporating barium zirconium sulfide (BZS) are investigated numerically as a photoabsorber with a back-contact layer of both crystalline and amorphous p+ type silicon, using a Solar Cell Capacitance Simulator software-1D. The titanium (Ti) alloyed BZS, which has an electron-energy bandgap close to optimum for a single junction PV device, is also investigated. A systematic study is carried out by varying the thickness, doping density, and defect density in the BZS layer. Among the two phases of Si, the amorphous one results in higher photoconversion efficiency (PCE) due to a favorable energy band alignment with BZS. The corresponding best PCEs predicted from the study are 19.7% for BZS films and 30% for Ba(Zr0.95Ti0.05)S3 films with the amorphous Si as back-contact.

The mechanism suggested by Turing for reaction-diffusion systems is widely used to explain pattern formation in biology and in many other areas. The persistence of patterns in altering environments is an important property in many natural cases. The experimental study of these phenomena can be done in chemical systems using appropriately designed reactors, e.g., in two-side-fed open gel reactors. This configuration allows for testing the effect of time-periodic boundary conditions that generate periodic feeding of chemicals on the dynamics of Turing patterns. The numerical approach is based on a chemically realistic mechanism and a 2D description of the reactor that reproduces the feeding from the boundaries and the corresponding concentration gradients. Depending on the amplitude and the frequency of the forcing, two basic regimes are observed, spatiotemporal oscillations and pulsating spot pattern. In between them, a mixed-mode pattern can also develop. Spot patterns can survive large amplitude forcing. The dynamics of the spot pulsation are analyzed in detail, considering the effect of the tanks and the chemical gradients that localize the patterns. These findings suggest that periodic feeding effectively controls pattern formation in chemical systems.

Boron neutron capture therapy (BNCT) is a radiation therapy that selectively kills cancer cells at the cellular level using the boron neutron capture reaction (BNCR) (10B(n.α)7Li). The amount of boron 10B delivers in boronophenylalanine (BPA)-BNCT to achieve anti-tumor effects is ≈15–40 ppm. The same is true for all boron drugs; however, whether the same amount of 10B is required for other boron drugs with different accumulation characteristics has not been intensively investigated. Therefore, herein, a virtual cell model with intracellular organelles is prepared, and the BPA equivalent dose concentration to the cell nucleus is analyzed using particle and heavy ion transport code system-based microdosimetry. Additionally, the intranuclear minimal region (IMR) is set as a reference for the concept of the intranuclear domain in the microdosimetric kinetic model, and the BPA equivalent dose concentration to the IMR is estimated. The required boron delivery dose greatly varies depending on the dose assessment based on the accumulation characteristics of boron agents in intracellular organelles. Evaluation of the BNCR effect according to the accumulation characteristics without being influenced by the specified value of 15–40 ppm is recommended.

The manipulation of pressure and structural composition can result in tuned properties of a material to enhance its functionality. In this regard, the pressure significance of the ternary Cd0.75Zn0.25S alloy is acknowledged in this study by changing in the range of 0–20 GPa using density functional theory (DFT) via a self-consistent field within a generalized gradient approximation (GGA) and GGA–modified Becke–Jhonson (mBJ). The results demonstrate that this ternary alloy exhibits cubic symmetry at all operating pressures, with the bandgap energy increasing from 2.81 to 3.17 eV as the pressure increases, demonstrating that it is a direct bandgap alloy. Another aspect of this study pertains to the optical parameters of the material and their variations as a function of pressure. Here it is observed that absorption increases with increasing pressure, along with the static refractive index and static dielectric constant. As optical conductance is also increased, the findings of this study offer a theoretical framework for the Cd0.75Zn0.25S alloy for display applications in optoelectronic and photovoltaic devices operating at various pressure ranges.

2D half-metallic multiferroic materials have attracted great interest due to their rich novel performances and wide application prospect in nanoelectronics and nanodevices. Three intrinsic half-metallic multiferroic monolayers AV2S4 (A = Na, K, and Rb) are predicted based on the first-principles calculations. From the calculations, their ground states are all ferromagnetic and are completely spin-polarized at the Fermi level. Their total magnetic moments are always 3.00 μB per primitive cell, which origin mainly from V-ions. The two-center electronic structure t2g6↑t2g4↓eg1↑ of V-ions is proposed from the perspective of the crystal field theory to explain successfully the calculated magnetic moments. Interestingly, the alkali-metal ions are non-magnetic, but they may cause important influence on the magnetic coupling between transition ions, and then the half-metallicity of these monolayers. Their electric and magnetic properties may be tuned by their charge states and strains. Especially, their half-metallic stability may be evidently improved by the tensile strains. The half-metallic gaps of these monolayers may be increased by about 67.4%, 50.8%, and 32.4% for NaV2S4, KV2S4, and RbV2S4 by the tensile strain 2.0%, respectively.

Technology computer-aided design (TCAD) simulation is an important tool for the development of semiconductor devices. Based on coupled partial differential equations (PDEs) for behaviors, TCAD can calculate objects such as impurities, point defects, and electronic carriers in semiconductors. However, over recent years semiconductor devices have become increasingly miniaturized and complicated, resulting in much longer calculation times for TCAD. Machine learning is one technology that may be used to overcome this simulation cost problem. In this study, a neural network architecture is proposed that considers the structure of the coupled PDEs. Features representing each concentration distribution of the calculation objects are extracted by convolution operations and their reaction is modeled by channel attention. The performance of the proposed architecture and of conventional neural network models is evaluated using a simulation dataset generated by 1D coupled PDEs that models the diffusion and reaction of vacancies and interstitial atoms. In addition, the advantage of the method is discussed through the analyses of error correlations of the two predictions and attention coefficients. The machine learning method developed in this study will be applicable to other physics described by coupled PDEs and is expected to speed up the computation of simulations in various fields.

The grading capacity of lithium-ion battery is an important basis for evaluating battery quality. Aiming at the difficulty of determining the critical control factors and the threshold of the control parameters in the lithium-ion battery manufacturing process, a capacity prediction and process parameter optimization model of lithium-ion battery is proposed by combining the back propagation (BP) and particle swarm optimization (PSO) algorithms. First, the BP method is applied to establish the nonlinear mapping relationship between process data and grading capacity, which is regarded as the capacity consistency prediction model. Second, using the prediction model as fitness function and combining with PSO algorithm, the optimization model of process parameters is established. Finally, under the given initial process parameters from the lithium-ion battery pilot line, it is carried out to obtain the best process parameter formula. The results show that the BP method has an accurate capacity consistency prediction effect. Combined with PSO algorithm, the optimized process parameters are obtained, which significantly improves the capacity consistency of lithium-ion batteries. The results serve as an engineering application method to guide the selection and confirmation of process parameters at the battery design stage.

Data-guided methodologies for designing new materials are developing apace, yet advances for organic crystals have been infrequent. For the case of organic crystals, data-guided design platforms involve two problems: the need to construct regression models that can predict solid-state properties from molecular structures alone and the vast number of molecular structures that satisfy a targeted solid-state property. In this paper, a new pipeline is presented for designing molecules with targeted solid-state electronic properties. Here, the first problem is overcome by means of a novel visualization method for band structure data, which allows the user to specify their design preferences and simplify the creation of the required regression models. The second problem is overcome by allowing for user intervention in the selection of the final molecule for subsequent synthesis. The effectiveness of this pipeline is demonstrated by using it to design a simple new molecule with a targeted bandgap and impressive band dispersion. This pipeline is the first data-guided method that can design new molecules on the basis of genuine solid-state electronic properties, and with its emphasis on user-inclusivity, it can easily be applied in collaborative or industry settings.

Cesium lead iodide (CsPbI3) has attracted a great deal of attention as an absorption layer material for perovskite solar cells (PSCs) with high stability and suitable band gap (1.72 eV). In response to the problems of defect-induced nonradiative compounding and voltage loss caused by the common perovskite layer, the common strategies of interfacial engineering, altering crystal equivalence, and other modifications involve more complex processes and higher fabrication costs. In order to simplify the process and save costs, this work has omitted the electron transport layer (ETL), while still maintaining a high power conversion efficiency (PCE). This work has simulated PSCs with CsPbI3 (electron transport layer free) and have matched Cu2O as the most suitable hole transport layer (HTL) material. By simulating and optimizing the thickness and defect density of perovskite absorption layer and the defect density of interface defect layer (IDL1, IDL2), and determining the most suitable operating temperature, the PCE of the device can reach 18.8%, which is consistent with the experimental data. The asymmetric effect of the interface defect layer obtained in this work is similar to previous research reports. This research provides an economical solution for high-performance inorganic perovskite solar cells.

A dynamic view of the evolution of the infections of SARS-CoV-2 in Catalonia using a Digital Twin approach that forecasts the true infection curve is presented. The forecast model incorporates the vaccination process, the confinement, and the detection rate, and virtually allows to consider any nonpharmaceutical intervention, enabling to understand their effects on the disease's containment while forecasting the trend of the pandemic. A continuous validation process of the model is performed using real data and an optimization model that automatically provides information regarding the effects of the containment actions on the population. To simplify this validation process, a formal graphical language that simplifies the interaction with the different specialists and an easy modification of the model parameters are used. The Digital Twin of the pandemic in Catalonia provides a forecast of the future trend of the SARS-CoV-2 spread and information regarding the true cases and effectiveness of the NPIs to control the SARS-CoV-2 spread over the population. This approach can be applied easily to other regions and can become an excellent tool for decision-making.

Micromixers play a crucial role in the microreactions to offer rapid and homogenous mixing. In article number 2200671, Bo Liu, Jing Jin, Yonggang Zhu, and co-workers innovatively develop a high-performance 3D micromixer with helical elements over an ultra-wide range of Reynolds numbers to achieve efficient mixing, and build a validated coupled model to systematically investigate flow patterns and mixing characteristics of the micromixer.

An adaptive robust control method is proposed for underactuated mechanical systems (UMSs) with uncertainty in the paper. UMSs are supposed to follow prescribed constraints when there is uncertainty. Inspired by the Udwadia–Kalaba modeling approach, those constraints are considered as control objectives realized by robust control. The uncertainty considered in UMSs is characterized as time-varying and bounded, albeit with unknown bounds. In order to estimate the boundary information, a novel adaptive law is developed. By using the Lyapunov function, the UMS is guaranteed to be uniform boundedness and uniform ultimate boundedness. The simulation is validated by using a two-wheeled bicycle as an example, compared to conventional PD control methods. The simulation comparison results show that the proposed control method exhibits notable characteristics, including fast transient response, small overshoots, and good robust performance.

Searching for novel two-dimensional magnets using AI

The number of 2D systems based on Mn and C atoms has been growing significantly due to the magnetic and electronic properties that these monolayers present. In this work, a new Mn-C monolayer by density functional theory is proposed. The MnC4 stability is studied by the formation energy formalism to compare with other reported Mn-C monolayers. Our analysis points that the MnC4 monolayer stabilizes at extremely rich C conditions. Also, the MnC4 monolayer possess positive phonon branches, evidencing its dynamic stability. Further, it confirms that this monolayer is also stable up to 300 K. This new monolayer has an antiferromagnetic alignment with spin-channels along the [-110] direction and metallic behavior, being a strong candidate in the formation of exchange-biased systems when forming a van der Waals heterojunction with other Ferromagnetic materials.

The innovative lead-free formamidinium tin-based perovskite solar cell structure is considered nontoxic and potentially more stable than lead-based, although its performance is not yet excellent. This research aims to enhance the power conversion efficiency of perovskite solar cells and reduce the recombination losses. According to device modeling, the FASnI3 perovskite solar cell demonstrates a packing conversion efficiency of 14.3% (open circuit voltage (Voc) = 0.899 V, fill factor (FF) = 58.9%, and current density (Jsc) = 26.06 mA cm−2) by employing Bi hole transporting layers, a copper oxide, and crystalline silicon layers. Some features that affect the device include the thickness of each layer, the doping density of copper oxide and a silicon layer, and the back contact metalwork function. It is proposed that Bi-HTL reduce the carriers to enter hole transport layer (HTL) as the doping change so that decreasing charge carriers recombination and enhancing the device efficiency in tin-based perovskite solar cell with the structure of ITO/TiO2/FASnI3/CuO/Si/C. Furthermore, the impacts of various charge transport layers on energy band alignment, recombination, electric field, and IV properties are thoroughly explored.

The understanding of crystal formation in thin films and the precise knowledge of the relation between structure and surface diffusion are two important requirements for the efficient (nano)fabrication of organic electronic devices. Here a computational approach for simulating vapor-phase deposition is employed to obtain and investigate three types of crystalline thin films on graphite. All systems, namely pentacene, perfluoropentacene, and their 1:1 blend, which forms an alternate cocrystal, are constituted by recumbent molecules in accordance with experimental findings. The contributions of intermolecular interactions and of molecular rearrangements occurring during the deposition are analyzed to rationalize the final morphologies. Then, the generated structures are employed to evaluate the energy barriers that prevent molecular diffusion at terraces and step-edges, and to study the reorganization of the films upon high-temperature annealing. The broad agreement with experimental observations and the possibility of evaluating the potential energy surface at the molecular detail render the proposed approach a promising tool to make predictions for other systems.

To improve the uniformity of asphalt mixtures, optimizing the particle distribution is essential, and the screw conveyor of paver plays a critical role in this process. In this regard, a parameter optimization strategy based on the discrete element method and response surface methodology (RSM) is proposed. First, a discrete element model is developed for spiral material separation and asphalt mixture particles. Next, uniformity evaluation indicators are used to analyze the impact of individual parameters on particle uniformity. Then, simulation experiments are conducted under varying material level coefficients and rotational speeds, and a regression model is developed to analyze the impact of coupling factors using RSM. Afterward, different algorithms are employed to optimize the parameters of the model, and the results show that the particle swarm optimization algorithm is better than the traditional hill climbing algorithm. Last, field tests are conducted to verify the feasibility of the proposed optimization strategy. The optimal theoretical parameter of the material level coefficient is 0.95, and the rotational speed is 53 rpm. Overall, the proposed strategy offers an effective and accurate model for simulating the process of particle separation, which can lead to improved uniformity of asphalt mixtures.