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Unlocking the secrets of gas adsorption on pristine and defective magnesene (2D-Mg), a 2D material: Insights revealed through DFT and ML techniques with CO, CO2, and CH4 gases.

IoT devices powered by copper electrolyte-based dye-sensitized photovoltaic cells as ambient light harvesters achieve 38% power conversion efficiency and incorporate a dynamic intelligent on-device energy management system.

To evaluate the lumbar nerve root alterations in patients with lumbar disc herniation sciatica using advanced multimodality MRI sequences and the correlations with clinical and neurophysiological findings.We prospectively evaluated 45 patients suffering from unilateral lumbar radiculopathy due to disco radicular conflict. All patients underwent MRI examinations using a standard MRI protocol and additional advanced MRI sequences (DWI, DTI, and T2 mapping sequences). Relative metrics of ADC, FA, and T2 relaxation times were recorded by placing ROIs at the pre-, foraminal, and post-foraminal level, either at the affected side or the contralateral side, used as control. All patients were also submitted to electromyography testing, recording the spontaneous activity, voluntary activity, F wave amplitude, latency, and motor evoked potentials (MEP) amplitude and latency, both at the level of the tibialis anterior and the gastrocnemius. Clinical features (diseases duration, pain, sensitivity, strength, osteotendinous reflexes) were also recorded.Among clinical features, we found a positive correlation of pain intensity with ADC values of the lumbar nerve roots. The presence of spontaneous activity was correlated with lower ADC values of the affected lumbar nerve root. F wave and MEP latency were correlated with decreased FA values at the foraminal level and increased values at the post-foraminal level. The same neurophysiological measures correlated positively with pre-foraminal T2 mapping values and negatively with post-foraminal T2 mapping values. Increased T2 mapping values at the foraminal level were correlated with disease duration.Evaluation of lumbar nerve roots using advanced MRI sequences may provide useful clinical information in patients with lumbar radiculopathy, potentially indicating active inflammation/myelinic damage (DTI, T2 mapping) and axonal damage/chronicity (DWI).

Kinetic Monte Carlo (kMC) simulations are a well-established tool for investigating the operation of electrochemical systems. Standard kMC algorithms become unfeasible in the presence of processes on vastly different time scales. In electrochemical systems, such time scale disparities often arise between fast transport processes and slow electrochemical reactions. A promising approach to overcome time scale disparities in kMC models is given by temporal acceleration schemes. In this work, we present a local temporal acceleration scheme to bridge the time scale disparity between fast transport and slow reaction dynamics. We combine the superbasin concept with a local, particle-based criterion for the quasi-equilibrium detection and a partitioning of transitions and particles in the system into process chains. Scaling of entire quasi-equilibrated process chains considerably reduces the computational effort without disturbing the relative dynamics of transitions within a process chain. The methodology is outlined for a hybrid organic-aqueous electrolyte device which links fast electronic processes in an organic semiconductor with slow reduction reactions at its interface to the electrolyte. Our approach captures local inhomogeneities such that local physical quantities can be reproduced accurately. Additionally, we show that previous accelerated superbasin algorithms are limited by the presence of spatially varying time scale disparities. Our algorithm achieves an acceleration of several orders of magnitude providing a serious alternative to replace existing multiscale models by stand-alone kMC simulations.

Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up

First-principles simulations reveal the competition of defect formation and healing at grain boundaries in lead-halide perovskites. Fast halide migration at GBs mediates structural healing, but also gives rise to enhanced Frenkel defect formation.

MoS2 based materials are recognized as the promising candidate for multifunctional applications due to its unique physicochemical properties. But presence of lower number of active sites, poor electrical conductivity, and less stability of 2H and 1T MoS2 inherits its practical applications. Herein, we synthesized the Se inserted mixed-phase 2H/1T MoS2 nanosheets with abundant defects sites to achieve improved overall electrochemical activity. Moreover, the chalcogen insertion induces the recombination of photogenerated excitons and enhances the life of carriers. The bifunctional energy storage and photocatalytic pollutant degradation studies of the prepare materials are carried out. Fabricated symmetric solid-state supercapacitor showed an exceptional capacitance of 178 mF cm−2 with an excellent energy density of 8 μWh cm−2 and power density of 137 mW cm−2, with remarkable capacitance retention of 86.34% after successive 8000 charge–discharge cycles. The photocatalytic dye degradation experiments demonstrate that the prepared Se incorporated 1T/2H MoS2 is a promising candidate for dye degradation applications. Further, the DFT studies confirmed that the Se inserted MoS2 is a promising electrode material for supercapacitor applications with higher CQ due to a larger density of states near Fermi level as compared to pristine MoS2.

Interface engineering plays an important role in performance improvement for bulk-heterojunction organic solar cells (OSCs). The charge carrier dynamics and energetic landscape at the donor:accepto...

Dilute donor organic solar cells (OSCs) aim to circumvent the trade-off between open-circuit voltage Voc and short-circuit current density jsc by decreasing the donor/acceptor interface. The main c...

The same amount of non-ionizing energy is deposited in GaAs solar cells through 1 MeV proton and electron radiation at specific fluence values. The defects created are detected and characterized via temperature-dependent dark I–V analysis, and the energy levels are correlated to trap states observed via admittance spectroscopy. A remarkable difference is observed between the defect energy levels introduced in the proton and electron cases: in the former, the recombination centers lie around the mid-gap position, while in the latter they are spread over a wider energy range in the band-gap. This induces a profound difference in the degradation of the recombination current in the space-charge region. On the other hand, the degradation of the diffusion current in the neutral regions is found to be determined by the recombination velocities at the back and front hetero-interfaces of the solar cell. They depend only on the displacement damage dose and are independent of the particle type.

Organic semiconductor devices promise cost-efficient processability at low temperatures, but the usually amorphous materials suffer from low charge carrier mobility. The search for high mobility organic semiconductor materials has thrived data science and Machine Learning approaches to screen the vast amount of possible organic materials. We present a multiscale simulation model based on machine learned transfer integrals to compute the charge carrier mobility in organic thin films.

Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fast-evolving method that allows for analyzing big data and unveiling correlations that otherwise would remain undiscovered. It may hold invaluable potential to engineer novel functional materials with desired properties, a field, which is currently limited by time-consuming trial and error approaches and our limited understanding of how different material properties depend on each other. Here, we apply machine learning algorithms to classify complex biological materials based on their microtopography. With this approach, the surfaces of different variants of biofilms and plant leaves can not only be distinguished but also correctly classified according to their wettability. Furthermore, an importance ranking provided by one of the algorithms allows us to identify those surface features that are critical for a successful sample classification. Our study exemplifies how machine learning can contribute to the analysis and categorization of complex surfaces, a tool, which can be highly useful for other areas of materials science, such as damage assessment as well as adhesion or friction studies.

This work presents a novel theoretical description of the nonequilibrium thermodynamics of charge separation in organic solar cells (OSCs). Using stochastic thermodynamics, we take realistic state populations derived from the phonon-assisted dynamics of electron-hole pairs within photoexcited organic bilayers to connect the kinetics with the free energy profile of charge separation. Hereby, we quantify for the first time the difference between nonequilibrium and equilibrium free energy profile. We analyze the impact of energetic disorder and delocalization on free energy, average energy, and entropy. For a high disorder, the free energy profile is well-described as equilibrated. We observe significant deviations from equilibrium for delocalized electron-hole pairs at a small disorder, implying that charge separation in efficient OSCs proceeds via a cold but nonequilibrated pathway. Both a large Gibbs entropy and large initial electron-hole distance provide an efficient charge separation, while a decrease in the free energy barrier does not necessarily enhance charge separation.

One major factor limiting the efficiency in organic solar cells (OSCs) is the low open-circuit voltage (Voc). Existing theoretical studies link the Voc with the charge transfer (CT) state and nonra...

Organic-inorganic (hybrid) perovskite semiconductors offer a wide range of bandgaps, low-cost deposition, and wide optical absorption, making them an ideal candidate for new photovoltaic devices. All-perovskite two terminal (2T) tandem solar cells have the potential to achieve high efficiency and at the same time offer cost-effective fabrication. In a 2T tandem cell, it is needed to optimize various device parameters such as bandgaps and thicknesses of the subcells, in order to make the best use of the available solar spectrum. In this study, we propose a drift-diffusion (DD) simulation model to optimize the bandgaps and thicknesses of the top and bottom cells in all-perovskite 2T tandem solar cell. Using our simulation model, we investigated the effect of interface and bulk traps, mobility, doping of the charge transport layers and contact workfunctions to the power conversion efficiency. We calculated up to 36.6% efficiency for an ideal device. We found that the traps at the interfaces and in the bulk perovskite films are the most important factor hampering the tandem cell efficiency. We predicted up to 29.8% efficiency for a device with recombination losses. By changing the mobility in the active material of the bottom cell we found that, the mobility plays an important role in determining the optimum thicknesses of the top and the bottom cells. Optimizing cathode workfunctions leads to a 3–4% improvement in the efficiency. Our study will help to understand the role of various factors limiting tandem cell efficiency and ways to optimize the device parameters to ensure the best performing all-perovskite 2T tandem solar cell.

Dye-sensitized solar cells (DSCs) have gained great attention in recent years due to their low-cost fabrication, flexibility and high power conversion efficiency. In a DSC, due to interfaces between the dye and the charge transport materials, the interface electrostatics becomes a key factor determining the overall performance of the cell. Liquid electrolyte based DSCs suffer low stability, electrolyte leakage and in some cases electrode corrosion. Replacing liquid electrolyte with a solid semiconducting material leads to poor interfacial contacts, hence the interface electrostatics becomes one of the limiting factors. In this work, we present a drift-diffusion (DD) and density functional theory (DFT) study of solid-state DSCs to investigate the electrostatics at the TiO2/organic dye/Spiro OMeTAD interface, and its impact to the adsorbed dye energy levels, its absorption spectrum and the related charge injection. In our 3D drift-diffusion model, we solve a set of drift-diffusion equations coupled to Poisson equation for electrons, holes, doping impurities and the interface traps simultaneously. After that, we use first principles DFT modeling of dye-sensitized interfaces in the presence of the calculated electric fields. We find that interface traps located below the conduction band edge of mesoporous TiO2 influence the accumulation of photogenerated holes and built-in electric field near the interface. The built-in electric field leads to change the energetics at the dye/TiO2 interface leading to poor charge injection from excited dye into the TiO2. The simulations were carried out for different electronic trap density in TiO2 and different doping levels in the Spiro OMeTAD hole transport layer. This study helps to a better understanding of interface electrostatics and its role in the charge injection mechanism of solid-state DSCs.

Organic field effect transistors (OFETs) show considerable variation from device to device and batch to batch. For the basic understanding of the microscopic origins of these variations, it is crucial to develop both experimental and theoretical methods that allow to localize ineffective regions in a device. Raster techniques, which manipulate conductivity locally, combined with global current readout may provide this information, e.g. it has been suggested that diffraction limited illumination of OFETs by laser scanning photo-current microscopy provides trap density distribution maps. However, the question arises whether local variation of conductivity is indeed suited for localization of such defects given that the detected photo-current passes through the whole network of conductance in the thin film device. In this study, we present a simulation model based on resistor networks to investigate the effect of defective regions in organic thin films. We show that varying conductances locally allows indeed to reconstruct the spatial distributions of ineffective areas. We also demonstrate how such simulations can be applied to interpret photo-current microscopy maps in terms of trap densities.

Screening combinatorial space for novel materials, such as perovskite-like ones for photovoltaics, has resulted in a high amount of simulated high-throughput data and analysis thereof. This study proposes a comprehensive comparison of structural fingerprint-based machine learning models on seven open-source databases of perovskite-like materials to predict band gaps and energies. It shows that none of the given methods, including graph neural networks, are able to capture arbitrary databases evenly, while underlining that commonly used metrics are highly database-dependent in typical workflows. In addition, the applicability of variance selection and autoencoders to significantly reduce fingerprint size indicates that models built with common fingerprints only rely on a submanifold of the available fingerprint space.

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The ability to control the energy levels in semiconductors is compelling for optoelectronic applications. In this study, we managed to tune the work function (WF) of halide perovskite semiconductors using self-assembled monolayers of small molecules to induce stable dipoles at the surface. The direction and intensity of the surface dipoles rely on specific molecule-to-surface interactions. Electron acceptor or donor molecules result in the positive or negative WF shifts up to several hundreds of meV. Our approach provides a versatile tool to control the WF of halide perovskite and adjust the energy level alignment at the interface with charge transport materials in perovskite-based optoelectronics. The impact on perovskite solar cells is reported and discussed in detail with the support of modelling. publishedVersion

COVID-19 is a disease caused by a severe respiratory syndrome coronavirus. It was identified in December 2019 in Wuhan, China. It has resulted in an ongoing pandemic that caused infected cases including many deaths. Coronavirus is primarily spread between people during close contact. Motivating to this notion, this research proposes an artificial intelligence system for social distancing classification of persons using thermal images. By exploiting YOLOv2 (you look at once) approach, a novel deep learning detection technique is developed for detecting and tracking people in indoor and outdoor scenarios. An algorithm is also implemented for measuring and classifying the distance between persons and to automatically check if social distancing rules are respected or not. Hence, this work aims at minimizing the spread of the COVID-19 virus by evaluating if and how persons comply with social distancing rules. The proposed approach is applied to images acquired through thermal cameras, to establish a complete AI system for people tracking, social distancing classification, and body temperature monitoring. The training phase is done with two datasets captured from different thermal cameras. Ground Truth Labeler app is used for labeling the persons in the images. The proposed technique has been deployed in a low-cost embedded system (Jetson Nano) which is composed of a fixed camera. The proposed approach is implemented in a distributed surveillance video system to visualize people from several cameras in one centralized monitoring system. The achieved results show that the proposed method is suitable to set up a surveillance system in smart cities for people detection, social distancing classification, and body temperature analysis.

This work presents a video-camera-based fire/smoke sensing technique for early warning in antifire surveillance systems. By exploiting R-CNN (Region Convolutional Neural Network), a detection technique is developed for the measurement of the smoke and fire characteristics in restricted video surveillance environments, both indoor (e.g. a railway carriage, container, bus wagon, homes, offices), or outdoor (e.g. storage or parking areas). The considered application scenario, to reduce costs, is composed of a single, fixed camera per scene, working in the visible spectral range already installed in a closed-circuit television system for surveillance purposes. The training phase is done with indoor and outdoor image sets, with both smoke and non-smoke scenarios to assess the capability of true-positive/true-negative detection and false-positive/false-negative rejection. To generate the training set, a Ground Truth Labeler app is used and applied to the open-access Firesense dataset, including tens of indoor and outdoor fire/ smoke scenes developed as the output of an FP7 project, plus other videos not publicly available, provided by Trenitalia during specific fire/smoke tests on railway wagons performed at their testing facility in Osmannoro, Italy. The achieved results show that the proposed R-CNN technique is suitable for the creation of a smart video-surveillance system for fire/smoke detection.

Organic Solar Cells (OSCs) have been extensively studied regarding the impact of morphology and energetic disorder on power conversion efficiency (PCE). The impact of energetic disorder is well understood but the effect of disorder in molecular coupling (off-diagonal) and energetic disorder at the interface still needs investigation. We present a kinetic Monte Carlo (kMC) model to study the role of interface energetics and off-diagonal disorder on solar cell performance. We first analyze the impact of different energetic disorder at the acceptor(A)-donor(D) interface on the device performance. Our results reveal that the interface properties control the Voc and efficiency. For higher interface disorder, recombination increases and the Voc is reduced. We further demonstrate the impact of variable off-diagonal disorder at fixed interface disorder on the device. The results show that at higher off-diagonal disorder, recombination, as well as Voc, is reduced. The presented results allow a better understanding of the importance of the structural order at the interface for OSCs.

Organic-inorganic hybrid perovskite (OIHP) solar cells are one of the fastest-growing solar cell technologies ever. OIHPs offer a wide range of bandgaps, wide optical absorption and low-cost deposition, making them an ideal candidate for solar cells. Despite the amazing properties of these materials, the perovskite solar cell performance is hampered by grain boundaries, traps, and hysteresis behavior of current-voltage (JV). Though the exact mechanism behind the hysteresis is still unknown, ionic movements in the perovskite film are considered to be the primary reason behind it. We present a drift-diffusion (DD) model to study the role of cation-mediated non-radiative recombination in the JV hysteresis in CH3NH3PbI3 cells. We study how the mobile cations can trap electrons leading to the JV hysteresis and poor performance of the cell. Impact of cation-electron trapping constant and the JV scan rate is investigated. We also simulate how the accumulation of mobile ions at the grain boundaries and interfaces limits the solar cell output current. When the ions are distributed in the bulk grains, they hardly affect the cell performance. When they start accumulating at the grain boundaries, the Jsc drops rapidly. The presented results give an insight into the effect of grain boundaries, and cation-mediated recombination as a possible reason for the JV hysteresis in perovskite solar cells.

The ability to predict material properties without the need of resource consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making such predictions, they are computationally too expensive at a large scale. The recent advancements in artificial intelligence and machine learning as well as availability of large quantum mechanics derived datasets enable us to train models on these datasets as benchmark and to make fast predictions on much larger datasets. The success of these machine learning models highly depends on the machine-readable fingerprints of the molecules that capture their chemical properties as well as topological information. In this work we propose a common deep learning based framework to combine different types of molecular fingerprints to enhance prediction accuracy. Graph Neural Network (GNN), Many Body Tensor Representation (MBTR) and a set of simple Molecular Descriptors (MD) were used to predict the total energies, Highest Occupied Molecular Orbital (HOMO) energies and Lowest Unoccupied Molecular Orbital (LUMO) energies of a dataset containing ~62k large organic molecules with complex aromatic rings and remarkably diverse functional groups. The results demonstrate that a combination of best performing molecular fingerprints can produce better results than the individual ones. The simple and flexible deep learning framework developed in this work can be easily adapted to incorporate other types of molecular fingerprints.

Temperature-dependent admittance spectroscopy is shown to be a valuable method to perform defect characterization in standardized 2 cm × 2 cm component cells, representative of the individual sub-cells in the triple-junction architecture GaInP/GaAs/Ge. The admittance spectra of the GaAs subcell irradiated with protons and electrons is analyzed in the range 90 K $T$ < 300 K. The signature of the irradiation-induced defect H1 is detected, located 0.29 eV above the valence band. An admittance model including the effect of a Gaussian-like distribution of defects in the band gap is adopted to fit the experimental data and to extract the electrical parameters of H1. Moreover, the signature of a second thermally-activated process is detected and ascribed to majority carrier transport over the barrier formed at the back-surface field region of the cell. A simple multi-conductance model is used to fit the measured admittance data and to extract the barrier height.

The 94% of Europeans believe that environmental protection is really important and this is the reason why the new “green” technologies have not to be only studied, but rather extended to automotive manufacturing. This article presents the operational procedure for sizing a photovoltaic panel, considering the Powertrain system of a new light-weight electric vehicle. Here is presented a selection of the components necessary to manage the energy transfer to a storage system, under the hypothesis of reasonableness about the recharge time of the accumulators themselves. The project includes the construction of a car shelter, or carport, with photovoltaic tiles recharging the full electric car battery, with an off-grid system, in order to define a whole zero emissions mobility system. The objective was to present a simple methodology for the choice of the size of a photovoltaic panel capable of supplying the energy required for one total daily recharge for a fully electric vehicle. At the end of this paper is reported a possible configuration based on the use of innovative photovoltaic tiles, integrated in the roof. Details on the final realization are also discussed.

Nowadays, there are several commercial devices available as car computer with their own functionalities and possibility of customization. Most of these systems are prohibitively expensive and closed-box without the possibility of expansion or modification, except at an additional cost. The aim of this paper is to presents a Car Dashboard that display interactively at real time the important information of the vehicle like speed, engine rpm, fuel, state of battery messages sent on the vehicle CAN bus via an OBD-II cable and processed by a Raspberry Pi 3 singleboard computer. We presented an implementation of a digital dashboard exploiting optimized instrument for graphical design on a low-cost embedded platform. The final device is low-cost and able to be adapted to other vehicles with low physical or software modification. Further future capability could be expanded with the addition of GPS module, IMUs etc.

Core-shell nanocatalyst activities are chiefly controlled by bimetallic material composition, shell thickness, and nanoparticle size. We present a machine learning framework predicting strain with site-specific precision to rationalize how strain on Pt core-shell nanocatalysts can enhance oxygen reduction activities. Large compressive strain on Pt@Cu and Pt@Ni induces optimal mass activities at 1.9 nm nanoparticle size. It is predicted that bimetallic Pt@Au and Pt@Ag have the best mass activities at 2.8 nm, where active sites are exposed to weak compressive strain. We demonstrate that optimal strain depends on the nanoparticle size; for instance, strengthening compressive strain on 1.92 nm sized Pt@Cu and Pt@Ni, or weakening compressive strain on 2.83 nm sized Pt@Ag and Pt@Au, can lead to further enhanced mass activities.