System Dynamics Research Articles

Dependence of interfacial mixing for thermally induced magnetization switching in Gd/Fe multilayers

The use of femtosecond laser pulses for ultrafast triggering of magnetization switching is of great interest in multilayer systems with great tunability. At present, the impact of interfacial mixing on magnetization switching has not been thoroughly investigated. In this paper, the impact of interfacial mixing on magnetization dynamics in multilayer systems is investigated by a combination of atomic spin dynamics and two-temperature models. Our results show that interfacial mixing in multilayer systems not only reduces the energy density required for magnetization switching but also expands the range of pulse durations that can trigger magnetization switching. In addition, we have investigated the dependence of the switching time on the interfacial mixing in multilayer systems. The results show that interfacial mixing can accelerate the process of magnetization dynamics, thus providing a theoretical basis for the design of faster speed optically controlled magnetic tunnel junctions.

Estimating reaction barriers with deep reinforcement learning1

Stable states in complex systems correspond to local minima on the associated potential energy surface. Transitions between these local minima govern the dynamics of such systems. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and isolating the relevant species in experiments is difficult. Most of the time, the system remains near a local minimum, with rare, large fluctuations leading to transitions between minima. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system’s dynamics highly sensitive to the calculated energy barriers. This work aims to formulate the problem of finding the minimum energy barrier between two stable states in the system’s state space as a cost-minimization problem. It is proposed to solve this problem using reinforcement learning algorithms. The exploratory nature of reinforcement learning agents enables efficient sampling and determination of the minimum energy barrier for transitions.

Collective behavior of active filaments with homogeneous and heterogeneous stiffness.

The collective dynamics of active biopolymers is crucial for many processes in life, such as cellular motility, intracellular transport, and division. Recent experiments revealed fascinating self-organized patterns of diverse active filaments, while an explicit parameter control strategy remains an open problem. Moreover, theoretical studies so far mostly dealt with active chains with uniform stiffness, which are inadequate in describing the more complicated class of polymers with varying stiffness along the backbone. Here, using Langevin dynamics simulations, we investigate the collective behavior of active chains with homogeneous and heterogeneous stiffness in a comparative manner. We map a detailed non-equilibrium phase diagram in activity and stiffness parameter space. A wide range of phase states, including melt, cluster, spiral, polar, and vortex, are demonstrated. The appropriate parameter combination for large-scale polar and vortex formation is identified. In addition, we find that stiffness heterogeneity can substantially modulate the phase behaviors of the system. It has an evident destructive effect on the long-ranged polar structure but benefits the stability of the vortex pattern. Intriguingly, we unravel a novel polar-vortex transition in both homogeneous and heterogeneous systems, which is closely related to the local alignment mechanism. Overall, we achieve new insights into how the interplay among activity, stiffness, and heterogeneity affects the collective dynamics of active filament systems.

Grassmann time-evolving matrix product operators: An efficient numerical approach for fermionic path integral simulations.

Developing numerical exact solvers for open quantum systems is a challenging task due to the non-perturbative and non-Markovian nature when coupling to structured environments. The Feynman-Vernon influence functional approach is a powerful analytical tool to study the dynamics of open quantum systems. Numerical treatments of the influence functional including the quasi-adiabatic propagator technique and the tensor-network-based time-evolving matrix product operator method have proven to be efficient in studying open quantum systems with bosonic environments. However, the numerical implementation of the fermionic path integral suffers from the Grassmann algebra involved. In this work, we present a detailed introduction to the Grassmann time-evolving matrix product operator method for fermionic open quantum systems. In particular, we introduce the concepts of Grassmann tensor, signed matrix product operator, and Grassmann matrix product state to handle the Grassmann path integral. Using the single-orbital Anderson impurity model as an example, we review the numerical benchmarks for structured fermionic environments for real-time nonequilibrium dynamics, real-time and imaginary-time equilibration dynamics, and its application as an impurity solver. These benchmarks show that our method is a robust and promising numerical approach to study strong coupling physics and non-Markovian dynamics. It can also serve as an alternative impurity solver to study strongly correlated quantum matter with dynamical mean-field theory.

Exploring the soft pinning effect in the dynamics and the structure-dynamics correlation in multicomponent supercooled liquids.

We study multicomponent liquids by increasing the mass of 15% of the particles in a binary Kob-Andersen model. We find that the heavy particles have dual effects on the lighter particles. At higher temperatures, there is a significant decoupling of the dynamics between heavier and lighter particles, with the former resembling a pinned particle to the latter. The dynamics of the lighter particles slow down due to the excluded volume around the nearly immobile heavier particles. Conversely, at lower temperatures, there is a coupling between the dynamics of the heavier and lighter particles. The heavier particles' mass slows down the dynamics of both types of particles. This makes the soft pinning effect of the heavy particles questionable in this regime. We demonstrate that as the mass of the heavy particles increases, the coupling of the dynamics between the lighter and heavier particles weakens. Consequently, the heavier the mass of the heavy particles, the more effectively they act as soft pinning centers in both high and low-temperature regimes. A key finding is that akin to the pinned system, the self-dynamics and collective dynamics of the lighter particles decouple from each other as the mass of the heavy particles has a more pronounced impact on the latter. We analyze the structure-dynamics correlation by considering the system under the binary and modified quaternary framework, the latter describing the pinned system. Our findings indicate that whenever the heavy mass particles function as soft pinning centers, the modified quaternary framework predicts a higher correlation.

Artificial intelligence techniques for dynamic security assessments - a survey

The increasing uptake of converter-interfaced generation (CIG) is changing power system dynamics, rendering them extremely dependent on fast and complex control systems. Regularly assessing the stability of these systems across a wide range of operating conditions is thus a critical task for ensuring secure operation. However, the simultaneous simulation of both fast and slow (electromechanical) phenomena, along with an increased number of critical operating conditions, pushes traditional dynamic security assessments (DSA) to their limits. While DSA has served its purpose well, it will not be tenable in future electricity systems with thousands of power electronic devices at different voltage levels on the grid. Therefore, reducing both human and computational efforts required for stability studies is more critical than ever. In response to these challenges, several advanced simulation techniques leveraging artificial intelligence (AI) have been proposed in recent years. AI techniques can handle the increased uncertainty and complexity of power systems by capturing the non-linear relationships between the system’s operational conditions and their stability without solving the set of algebraic-differential equations that model the system. Once these relationships are established, system stability can be promptly and accurately evaluated for a wide range of scenarios. While hundreds of research articles confirm that AI techniques are paving the way for fast stability assessments, many questions and issues must still be addressed, especially regarding the pertinence of studying specific types of stability with the existing AI-based methods and their application in real-world scenarios. In this context, this article presents a comprehensive review of AI-based techniques for stability assessments in power systems. Different AI technical implementations, such as learning algorithms and the generation and treatment of input data, are widely discussed and contextualized. Their practical applications, considering the type of stability, system under study, and type of applications, are also addressed. We review the ongoing research efforts and the AI-based techniques put forward thus far for DSA, contextualizing and interrelating them. We also discuss the advantages, limitations, challenges, and future trends of AI techniques for stability studies.

Study on the Effect of Cracks in Diaphragm Couplings on the Dynamic Characteristics of Shaft System

Diaphragm couplings are prone to developing diaphragm cracks under prolonged high-speed operating conditions, which can lead to degradation in the performance of the transmission system and affect the dynamics of the shafting system. To investigate the effects of diaphragm cracks on the dynamics of couplings and the shafting system, a finite element model of a diaphragm coupling with a crack failure is established using ANSYS finite element software to analyze the time-varying characteristics of the diaphragm coupling’s angular and radial stiffness. A shaft dynamics model of the diaphragm coupling with a crack is developed using Timoshenko beam elements to analyze the impact of different crack lengths and locations on the dynamics of the shafting system. The validity of the dynamic model for a diaphragm coupling with a crack is verified through a constant speed experiment conducted on a rotor test bench. The results indicate that diaphragm crack failure causes a change in the periodicity of the time-varying stiffness of the diaphragm coupling, leading to a distinct 2× component appearing in the frequency domain of the transmission shaft system.

Nitric Oxide-Dependent Regulation of Oxygen-Related Processes in a Rat Model of Lead Neurotoxicity: Influence of the Hypoxia Resistance Factor

Background/Aims: Lead exposure is known to induce oxidative stress and neurotoxicity. Nitric oxide (NO) plays an important role in modulating oxidative stress, with L-arginine as a precursor of NO and Nω-nitro-L-arginine (L-NNA) as an inhibitor of NO synthase, an enzyme that catalyses the production of nitric oxide (NO) from L-arginine. Methods: This study investigated the differential effects of L-arginine and L-NNA on markers of oxidative stress and biochemical changes in brain tissue from rats with different levels of resistance to hypoxia exposed to lead nitrate. Rats with either low or high resistance to hypoxia were exposed to lead nitrate (oral 3.6 mg lead nitrate/kg b.w. per day for 30 days) and treated with L-arginine (600 mg/kg b.w., i.p., 30 min before and after exposure to lead nitrate) or L-NNA (35 mg/kg b.w., i.p., 30 min before and after exposure to lead nitrate). Brain tissue samples were analysed for lipid peroxidation, oxidative modification of proteins, and activity of antioxidant enzymes, including superoxide dismutase, catalase, glutathione reductase, and peroxidase, and total antioxidant status (TAS). We also examined the biomarkers of biochemical pathways involving the activity of alanine and aspartate aminotransferases, succinate dehydrogenase (SDH), and α-ketoglutarate dehydrogenase (KGDH). In addition, the trend observed was supported by assessments of the acetylcholine levels and acetylcholinesterase activity (ACh-AChE system) in brain tissue. Results: In rats with low resistance to hypoxia, the L-arginine treatment significantly reduced lipid peroxidation and oxidative protein modification but increased antioxidant enzyme activity, suggesting a protective effect against lead-induced oxidative stress. Conversely, in rats with high resistance to hypoxia, L-NNA had a protective effect, reducing lead-induced oxidative damage and decreasing lipid peroxidation, whereas L-arginine exacerbated oxidative stress and impaired antioxidant defences. These findings were supported by corresponding changes in the acetylcholine-acetylcholinesterase system, reflecting the observed patterns of lead-induced oxidative stress and neurotoxicity. The study shows that L-arginine exerts a protective effect by reducing lead-induced oxidative damage via an improvement in TAS. Our study shows that lead nitrate exposure significantly increases ala-nine and aspartate aminotransferase activity in brain tissue, with L-arginine exacerbating and L-NNA reversing this effect. The lead nitrate exposure also affected the activities of SDH and KGDH, which are important for cellular energy production and hypoxia resistance, with L-arginine altering SDH activity depending on the level of resistance and L-NNA enhancing both SDH and KGDH activities. These trends were further validated by alterations in the ACh-AChE system, highlighting the differential role of NO-dependent mechanisms in modulating lead-induced neurotoxicity based on hypoxia resistance. Conclusion: These findings suggest potential targeted therapeutic strategies based on the oxidative stress profile and highlight the potential of nitric oxide system modulators in counteracting lead-induced biochemical alterations and the dynamics of the ACh-AChE system depending on the individual physiological reactivity of organisms.

A hybrid predictive modeling approach for catalyzed polymerization reactors

Polymerization reactions are characterized by complex, nonlinear behaviors that pose significant challenges for conventional modeling techniques. Accurate and reliable models are crucial for advancing material science and enabling technological innovations across various industries. Conventional first-principles models often fall short in capturing the intricate dynamics of polymeric systems, leading to limitations in predictive accuracy. In this work, we propose a novel hybrid modeling approach that combines a conventional first-principles model with the strengths of a data-driven multi-layer perceptron (MLP) model and also a linear regression (LR) model to enhance the predictability of polymerization processes. Utilizing this hybrid approach significantly reduces the mean absolute error for predicting the concentrations of main reagents by 84% and 86%, respectively, in experiments with significantly deviant outcomes. Our results indicate that the model is capable of predicting the concentrations of both the main and side products with a maximum error margin of 3.5%.

Integrating Multi-Cancer Early Detection (MCED) Tests with Standard Cancer Screening: System Dynamics Model Development and Feasibility Testing.

Cancer screening plays a critical role in early disease detection and improving outcomes. In Australia, established screening protocols for colorectal, breast and cervical cancer have significantly contributed to timely cancer detection. However, the recent introduction of multi-cancer early detection (MCED) tests arguably can disrupt current screening, yet the extent to which these tests provide additional benefits remains uncertain. We present the development and initial validation of a system dynamics (SD) model that estimates the additional cancer detections and costs associated with MCED tests. This article describes the development of a simulation model built to evaluate the additional patient diagnoses and the economic impact of incorporating MCED testing alongside Australia's well-established standard of care (SOC) screening programs for colorectal, breast, cervical and lung cancers. The model was designed to estimate the additional number of patients diagnosed at each cancer stage (stage I, II, III, IV, or unknown) and the associated costs. This simulation model allows for the analysis of multiple scenarios under a plausible set of assumptions regarding population-level participation rates. An SD model was developed to represent the existing SOC national cancer screening pathways and to integrate potential clinical pathways that could be introduced by MCED tests. The SD model was built to investigate three scenarios for the use of MCED testing: firstly, to explore the viability of MCED testing as a substitute among individuals who are not opting for SOC screening for any reason; secondly, to implement MCED testing exclusively for individuals ineligible for SOC screening, yet have high-risk characteristics; and thirdly, to employ MCED testing after SOC screening to serve as a triaging/confirmatory tool for individuals receiving inconclusive test results. The three primary scenarios were constructed by varying diagnostic accuracy and uptake rates of MCED tests. The clinical utility and outcomes of MCED testing for screening and early detection still lack comprehensive evidence. Nonetheless, this simulation model facilitates a thorough analysis of MCED tests within the Australian healthcare context, providing insights into potential additional detections and costs to the healthcare system, which may help prioritise future evidence development. The adaptable yet novel SD model presented herein is anticipated to be of considerable interest to industry, policymakers, consumers and clinicians involved in informing clinical and economic decisions regarding integrating MCED tests as cancer screening and early detection tools. The expected results of applying this SD model will determine whether using MCED testing in conjunction with SOC screening offers any potential benefits, possibly guiding policy decisions and clinical practices towards the adoption of MCED tests.

Endangered Commons? Modeling the Effects of Demographic Trends Coupled with Admission Rules to Common Property Institutions

This study investigates the long-term effects of demographic trends and admission rules on common properties in the Province of Trento, Italy, which we refer to as historical commons. Historical commons have evolved into socio-ecological systems over the centuries, meaning that communities governed collectively natural resources and lands essential for community survival. Communities and the admission rules that determine their composition are an important constituting element of historical commons because they have developed local ecological knowledge and practices of sustainable use of natural resources. Our study hypothesizes that commons continuity is endangered because of the declining trend of the size of communities being influenced by demographic trends coupled with admission rules. Grounding our research in systems dynamics, we use empirical data including demographic projections and existing admission rules to simulate their effect on the site of the community using the Province of Trento, Italy, as our study region. To achieve that, three types of historical commons are identified: open, semi-open, and closed, each with different admission criteria based on inheritance and/or residency. Results indicate that inheritance-based admission rules can significantly reduce the number of commoners over time, potentially endangering the continuity of these self-governance institutions. The study discusses the results in light of the literature on historical commons’ continuity to evaluate different policies affecting the size of the community grounding on different mental models. The study concludes that a simulation approach can promote an anticipatory approach to the co-design of policies to ensure inclusive continuity of historical commons.

Multi-Scenario Simulation of Land Use and Assessment of Carbon Stocks in Terrestrial Ecosystems Based on SD-PLUS-InVEST Coupled Modeling in Nanjing City

In the context of achieving the goal of carbon neutrality, exploring the changes in land demand and ecological carbon stocks under future scenarios at the urban level is important for optimizing regional ecosystem services and developing a land-use structure consistent with sustainable development strategies. We propose a framework of a coupled system dynamics (SD) model, patch generation land-use simulation (PLUS) model, and integrated valuation of ecosystem services and trade-offs (InVEST) model to dynamically simulate the spatial and temporal changes of land use and land-cover change (LUCC) and ecosystem carbon stocks under the NDS (natural development scenario), EPS (ecological protection scenario), RES (rapid expansion scenario), and HDS (high-quality development scenario) in Nanjing from 2020 to 2040. From 2005 to 2020, the expansion rate of construction land in Nanjing reached 50.76%, a large amount of ecological land shifted to construction land, and the ecological carbon stock declined dramatically. Compared with 2020, the ecosystem carbon stocks of the EPS and HDS increased by 2.4 × 106 t and 1.5 × 106 t, respectively, with a sizable ecological effect. It has been calculated that forest and cultivated land are the two largest carbon pools in Nanjing, and the conservation of both is decisive for the future carbon stock. It is necessary to focus on enhancing the carbon stock of forest ecosystems while designating differentiated carbon sink enhancement plans based on the characteristics of other land types. Fully realizing the carbon sink potential of each ecological functional area will help Nanjing achieve its carbon neutrality goal. The results of the study not only reveal the challenges of ecological conservation in Nanjing but also provide useful guidance for enhancing the carbon stock of urban terrestrial ecosystems and formulating land-use planning in line with sustainable development strategies.

Шкала родительского стресса: адаптация на русскоязычной выборке матерей школьников

<p style="text-align: justify;"><strong>Objective. </strong>Adaptation of the Parental Stress Scale by J. Berry and V. Jones on a sample of mothers of Russian schoolchildren – first and fourth grade students of elementary schools in Nizhny Novgorod and the Nizhny Novgorod region. <br><strong>Background. </strong>Modern parenting research highlights the complex nature of parenting experiences, the diversity of parental practices, the changes associated with the child's upbringing and the dynamics of the family system, and the combination of both positive and negative emotions in parenting. However, there is a noticeable shortage of tools in the Russian-language literature to measure both parental satisfaction and parental stress. <br><strong>Study design. </strong>The study was conducted using an online survey within the “Longitudinal study of factors of school failure”, implemented in Nizhny Novgorod and the Nizhny Novgorod region. <strong>Participants. </strong>The study included a sample of 900 mothers of fourth grade and 1120 mothers of the first grade school students. <strong>Measurements.</strong> Exploratory and confirmatory factor analysis and McDonald's Omega coefficients were used for psychometric data analysis. The PERMA Profiler questionnaire and correlation analysis were used to assess validity based on convergent and discriminant evidence. The data was analyzed in the Jamovi 2022 and R programs. <br><strong>Results. </strong>Factor analyses of the internal structure suggest two factors, including 8 items each. The subscales “Parental stress” and “Parental satisfaction” demonstrate a high level of internal consistency. The assessment of validity based on the convergent and discriminant evidence confirms the reliability and validity of the instrument. <br><strong>Conclusions. </strong>The scale demonstrates good psychometric properties and can be used for parenting research, as well as in applied projects to improve parental competence and emotional support for parents.</p>

Monitoring Water Depth and Seepage Rates in Pitcher Irrigation for Sustainable Crop Water Management

Pitcher irrigation is an ancient yet effective method for delivering water directly to plant root zones, particularly in regions facing water scarcity. The main objective of this study is to evaluate the efficiency of pitcher irrigation in conserving water and supporting brinjal crop growth. This study investigates the efficiency and dynamics of a pitcher irrigation system in supplying water to brinjal crops, utilizing nine porous clay pitchers buried near the plants. Water depth in the pitchers was monitored from March 1 to March 22, 2024, at three intervals daily. Initial water levels of 15 cm were recorded on March 1, followed by gradual declines in water depth over time, with some pots showing rapid depletion, particularly during peak daylight hours. Pot 4 exhibited the most significant water loss, likely due to increased soil absorption or higher crop water demand, while Pots 1 and 3 showed more consistent water retention. The study revealed significant variation in seepage rates, with higher water loss during the day, particularly in the afternoon, compared to slower overnight seepage. These findings emphasize the need for careful irrigation management, including tailored refill schedules, to ensure uniform water availability and prevent moisture deficits. The results demonstrate that pitcher irrigation can effectively sustain crop growth, but its efficiency depends on continuous monitoring and adjustments based on local conditions.

Analysis of metadynamics simulations by metadynminer.py.

Molecular dynamics simulation is very useful but computationally demanding method of studying dynamics of biomolecular systems. Many enhanced sampling methods were developed in order to obtain the desired results in available computational time. Metadynamics and its variants are common enhanced sampling methods used for this purpose. Metadynamics simulations allow the user to gather large amounts of data, which have to be analyzed to elucidate the properties of the studied system. Here, we present metadynminer.py, a Python package that allows easy and user-friendly analysis and visualization of the results obtained from metadynamics simulations. The built-in functions automate frequent tasks and make the package easy to use for new users, while its many customization options and object-oriented nature allow for integration into specialized data analysis workflows by more advanced users. The 'metadynminer.py' Python package is available under the GPL-3.0 license via PyPi and Conda. The development version is available on GitHub along with issue support (https://github.com/Jan8be/metadynminer.py). Documentation, tutorial and Jupyter Notebook (provided through the public mybinder.org service) are available at https://metadynreporter.cz.

Improving Communication Networks to Transfer Data in Real Time for Environmental Monitoring and Data Collection

Integrated communication networks (CN) have proven successful in tracking environmental activities, wherein several sensors are installed throughout diverse surroundings to gather data or observe certain events. CNs, comprising several interacting detectors, have proven effective in various applications by transmitting data via diverse transmission methods inside the communication system. The erratic and constantly changing surroundings necessitate conventional CNs to engage in frequent conversations to disseminate the latest data, potentially incurring substantial connection expenses through joint data gathering and dissemination. High-frequency communications are prone to failure due to the extensive distance of data transfer. This research presents a unique methodology for multi-sensor environmental monitoring networks utilizing autonomous systems. The transmission system can mitigate elevated communication costs and Single Point of Failing (SPOF) challenges by employing a decentralized method that facilitates in-network processing. The methodology employs Boolean systems, enabling a straightforward verification process while preserving essential details about the dynamics of the communication system. The methodology further simplifies the data collection process and employs a Reinforcement Learning (RL) technique to forecast future events inside the surroundings by recognizing patterns.

Effective modeling of open quantum systems by low-rank discretization of structured environments.

The accurate description of the interaction of a quantum system with its environment is a challenging problem ubiquitous across all areas of physics and lies at the foundation of quantum mechanics theory. Here, we pioneer a new strategy to create discrete low-rank models of the system-environment interaction, by exploiting the frequency and time domain information encoded in the fluctuation-dissipation relation connecting the system-bath correlation function and the spectral density. We demonstrate the effectiveness of our methodology by combining it with tensor-network methodologies and simulating the quantum dynamics of complex excitonic systems in a highly structured bosonic environment. The new modeling framework sets the basis for a leap in the analysis of open quantum systems, providing controlled accuracy at significantly reduced computational costs, with benefits in all connected research areas.

Thermomagnetic Anomalies by Magnonic Criticality in Ultracold Atomic Transport.

We investigate thermomagnetic transport in an ultracold atomic system with two ferromagnets linked via a magnetic quantum point contact. Using the nonequilibrium Green's function approach, we show a divergence in spin conductance and a slowing down of spin relaxation that manifest in the weak effective-Zeeman-field limit. These anomalous spin dynamics result from the magnonic critical point at which magnons become gapless due to spontaneous magnetization. Our findings unveil untapped dynamics in ultracold atomic systems, opening new avenues in thermomagnetism.

Neuro-modelling and fuzzy logic control of a two-wheeled wheelchair system

Two-wheeled wheelchairs have been used as alternatives for the elderly and disabled people to perform physical activities due to their restriction of movement. Significant challenges posed by two-wheeled wheelchairs control due to their inherent instability, resembling that inverted pendulum system. This research addresses these challenges by developing a dynamic non-linear model and stability control using computational algorithms. A neural network-based Nonlinear Autoregressive model with Exogenous Inputs (NARX) was developed, capturing and behaving similar to the complex dynamics of the wheelchair system with the identification on the experimental input–output data. Ensuring stable and responsive control, design of optimized PD-type and PID-type fuzzy logic controllers using Particle Swarm Optimization (PSO) were established and were tested under a simulation environment. The performance was evaluated across various metrics, including Integral Squared Error (ISE), Integral Absolute Error (IAE), Mean Squared (MSE), and Integral Time Absolute Error (ITAE). The result demonstrates that the PSO Optimized PID-type fuzzy logic controller with scaling factor from MSE index performance come out as best overall, significantly outperforms PD-type fuzzy logic controller, reducing its settling time by 12.5% to 35 s, minimizing overshoot to 0.81°, and achieving a negligible steady-state error of 0.046%. These results highlight the significant of integrating fuzzy logic control and PSO to the neural network model in enhancing the stability and performance of two-wheeled wheelchair systems, offering user safety and comfort.

Quantum Sensing of Spin Dynamics Using Boron-Vacancy Centers in Hexagonal Boron Nitride.

Spin defects embedded in solid-state systems are appealing for quantum sensing of materials and for quantum science and engineering. The spin-sensitive photoluminescence of optically active spin defects in Van der Waals based materials, such as the boron-vacancy (V_{B}^{-}) center in hexagonal boron nitride, enables its application as a quantum sensor to detect weak, spatially localized magnetic static and dynamic fields. However, the utility of V_{B}^{-} centers to probe spin dynamics in magnetic systems has yet to be demonstrated; this is essential to establish the V_{B}^{-} as a modular sensing platform that can be seamlessly integrated with emergent quantum materials to probe a wide range of static and dynamic phenomena. Here, we use V_{B}^{-} centers to experimentally probe uniform mode magnon dynamics and optically perform ferromagnetic resonance spectroscopy on a thin magnetic film.