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Renewable Energy Sources (RES) showed enormous growth in the last few years. In comparison with the other RES, solar power has become the most feasible source because of its unique properties such as clean, noiseless, eco-friendly nature, etc. During the extraction of electric power, the DC–DC converters were given the prominent interest because of their extensive use in various applications. Photovoltaic (PV) systems generally suffer from less energy conversion efficiency along with improper stability and intermittent properties. Hence, there is a necessity of the Maximum power point tracking (MPPT) algorithm to ensure the maximum power available that can be harnessed from the solar PV. In this paper, the most important features of the DC/DC converters along with the MPPT techniques are reviewed and analyzed. A detailed comprehensive analysis is made on different converter topologies of both non-isolated and isolated DC/DC converters. Then, the modulation strategies, comparative performance evaluation are addressed systematically. At the end, recent advances and future trends are described briefly and considered for the next-generation converter’s design and applications. This review work will provide a useful structure and reference point on the DC/DC converters for researchers and designers working in the field of solar PV applications.

In materio computing offers the potential for widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and interaction. While individual devices offer basic data processing capabilities, networks of interconnected devices can perform more complex and varied tasks. However, designing such networks for dynamic tasks is challenging in the absence of physical models and accurate characterization of device noise. We introduce the Noise-Aware Dynamic Optimization (NADO) framework for training networks of dynamical devices, using Neural Stochastic Differential Equations (Neural-SDEs) as differentiable digital twins to capture both the dynamics and stochasticity of devices with intrinsic memory. Our approach combines backpropagation through time with cascade learning, enabling effective exploitation of the temporal properties of physical devices. We validate this method on networks of spintronic devices across both temporal classification and regression tasks. By decoupling device model training from network connectivity optimization, our framework reduces data requirements and enables robust, gradient-based programming of dynamical devices without requiring analytical descriptions of their behaviour. Dynamic systems show promise for physical neural networks, but gradient based optimization requires mathematical models. Here, the authors present a data-driven framework for optimizing networks of arbitrary dynamic systems which is robust to noise, and enables tasks such as neuroprosthetic control.

X-ray Photoelectron Spectroscopy (XPS) is a powerful tool for probing the chemical and electronic states of materials with elemental specificity and surface sensitivity. However, its application in the tender X-ray range (1–5 keV) for synchrotron radiation has remained limited due to the limited choice of optics capable of maintaining high reflectivity and efficiency in this energy window. To address this, multilayer (ML) grating structures have become increasingly popular, offering significantly higher efficiency than SL coatings in the tender X-ray region. This paper presents the development of ML laminar gratings optimised for enhancing efficiency in the tender X-ray range, and capable of retaining performance under intense X-ray exposure in the oxygen partial pressure of 10 mbar. The ML coating quality was verified through X-ray reflectivity (XRR), XPS and near-edge X-ray absorption fine structures (NEXAFS) measurements, while the performance of the grating was validated through beamline flux transmission and XPS measurements. The MLLG demonstrated 22 higher intensity in flux and XPS, significantly improving the signal-to-noise ratio. Most importantly, the MLLGs outperformed traditional designs by offering improved spectral resolution while maintaining measurement capability at varying values without compromising the intensity. Furthermore, we demonstrated that the incorporation of nitrogen during deposition further enhances flux transmission.

The emergence of thin film CoFeB has driven research and industrial applications in the past decades, with the magnetic random access memory (MRAM) the most prominent example. Because of its beneficial properties, it fulfills multiple functionalities as information-storing, spin-filtering, and reference layer in magnetic tunnel junctions. In future, this versatility can be exploited beyond the traditional applications of spintronics by combining with advanced materials, such as oxide-based materials. Pulsed laser deposition (PLD) is their predominant growth-method, and thus the compatibility of CoFeB with this growth technique will be tested here. This encompasses a comprehensive investigation of the structural and magnetic propoperties. In particular, we find a substantial 'dead' magnetic layer and confirm that it is caused by oxidation employing the x-ray magnetic circular dichroism (XMCD) effect. The low damping encountered in vector network analyzer-based ferromagnetic resonance (VNA-FMR) renders them suitable for magnonics applications. These findings demonstrate that CoFeB thin films are compatible with emergent, PLD-grown materials, ensuring their relevance for future applications.

The NaZn13 type itinerant magnet LaFe13−xSix has seen considerable interest due to its unique combination of large magnetocaloric effect and low hysteresis. Here, this alloy with a combination of magnetometry, bespoke microcalorimetry, and inelastic neutron scattering is investigated. Inelastic neutron scattering reveals the presence of broad quasielastic scattering that persists across the magnetic transition, which is attributed to spin fluctuations. In addition, a quasielastic peak is observed at Q = 0.52 Å−1 for x = 1.2 that exists only in the paramagnetic state in proximity to the itinerant metamagnetic transition and argue that this indicates emergence of a hidden mag the netic phase that drives the first‐order phase transition in this system. The intermetallic LaFe13‐xSix is an itinerant magnet that exhibits a shift from a first‐order to a second‐order phase transition with temperature, magnetic field, and Si content. The evolution of this first‐order phase transition with inelastic neutron scattering is investigated, observing quasielastic scattering that is attributed to spin fluctuations and the emergence of a hidden magnetic phase.

In-materia reservoir computing (RC) leverages the intrinsic physical responses of functional materials to perform complex computational tasks. Magnetic metamaterials are exciting candidates for RC due to their huge state space, nonlinear emergent dynamics, and non-volatile memory. However, to be suitable for a broad range of tasks, the material system is required to exhibit a broad range of properties, and isolating these behaviours experimentally can often prove difficult. By using an electrically accessible device consisting of an array of interconnected magnetic nanorings -- a system shown to exhibit complex emergent dynamics -- here we show how reconfiguring the reservoir architecture allows exploitation of different aspects the system's dynamical behaviours. This is evidenced through state-of-the-art performance in diverse benchmark tasks with very different computational requirements, highlighting the additional computational configurability that can be obtained by altering the input/output architecture around the material system.

The NaZn 13 type itinerant magnet LaFe 13− x Si x has seen considerable interest due to its unique combination of large magnetocaloric effect and low hysteresis. Here, this alloy with a combination of magnetometry, bespoke microcalorimetry, and inelastic neutron scattering is investigated. Inelastic neutron scattering reveals the presence of broad quasielastic scattering that persists across the magnetic transition, which is attributed to spin fluctuations. In addition, a quasielastic peak is observed at Q = 0.52 Å −1 for x = 1.2 that exists only in the paramagnetic state in proximity to the itinerant metamagnetic transition and argue that this indicates emergence of a hidden mag the netic phase that drives the first‐order phase transition in this system.

Neural networks have revolutionized the area of artificial intelligence and introduced transformative applications to almost every scientific field and industry. However, this success comes at a great price; the energy requirements for training advanced models are unsustainable. One promising way to address this pressing issue is by developing low-energy neuromorphic hardware that directly supports the algorithm's requirements. The intrinsic non-volatility, non-linearity, and memory of spintronic devices make them appealing candidates for neuromorphic devices. Here we focus on the reservoir computing paradigm, a recurrent network with a simple training algorithm suitable for computation with spintronic devices since they can provide the properties of non-linearity and memory. We review technologies and methods for developing neuromorphic spintronic devices and conclude with critical open issues to address before such devices become widely used.

We describe 'RingSim', a phenomenological agent-based model that allows numerical simulation of magnetic nanowire networks with areas of hundreds of micrometers squared for durations of hundreds of seconds; a practical impossibility for general-purpose micromagnetic simulation tools. In RingSim, domain walls (DWs) are instanced as mobile agents which respond to external magnetic fields, and their stochastic interactions with pinning sites and other DWs are described via simple phenomenological rules. We first present a detailed description of the model and its algorithmic implementation for simulating the behaviours of arrays of interconnected ring-shaped nanowires, which have previously been proposed as hardware platforms for unconventional computing applications. The model is then validated against a series of experimental measurements of an array's static and dynamic responses to rotating magnetic fields. The robust agreement between the modelled and experimental data demonstrates that agent-based modelling is a powerful tool for exploring mesoscale magnetic devices, enabling time scales and device sizes that are inaccessible to more conventional magnetic simulation techniques.

Physical computing has the potential to enable widespread embodied intelligence by leveraging the intrinsic dynamics of complex systems for efficient sensing, processing, and interaction. While individual devices provide basic data processing capabilities, networks of interconnected devices can perform more complex and varied tasks. However, designing networks to perform dynamic tasks is challenging without physical models and accurate quantification of device noise. We propose a novel, noise-aware methodology for training device networks using Neural Stochastic Differential Equations (Neural-SDEs) as differentiable digital twins, accurately capturing the dynamics and associated stochasticity of devices with intrinsic memory. Our approach employs backpropagation through time and cascade learning, allowing networks to effectively exploit the temporal properties of physical devices. We validate our method on diverse networks of spintronic devices across temporal classification and regression benchmarks. By decoupling the training of individual device models from network training, our method reduces the required training data and provides a robust framework for programming dynamical devices without relying on analytical descriptions of their dynamics.