Yuan-Tao Zhang

Fast surrogate modeling of atmospheric radio-frequency (RF) plasma fluid systems is useful for accelerating parameter scans and supporting rapid system design. However, traditional discretization methods, such as finite difference or finite element methods, remain computationally expensive when repeated evaluations are required under multiple discharge conditions. In this work, a Physics-Informed Deep Operator Network (PI-DeepONet) is developed and assessed using a simplified one-dimensional fluid model of atmospheric RF plasmas as a proof-of-principle benchmark. In the present architecture, the network is conditioned on voltage-specific (300–500 V) initial electron and ion density profiles, rather than on the applied voltage amplitude as an explicit scalar input. Therefore, for each voltage-conditioned prediction, the corresponding initial density profiles must be supplied to the network. Given these externally supplied initial profiles, a single trained model can predict plasma discharge dynamics across the entire spatiotemporal domain, including electron and ion densities, particle fluxes, electric potential, and electric field distributions. Numerical verification against finite-difference results shows that the global relative L 2 errors of all benchmark fields are below 2.1%, while the inference time is reduced by more than 300 times compared with the reference solver. Region-bounded error analysis further shows that the sheath-region relative L 2 errors remain below 5.0%, with the largest localized discrepancies occurring near the sheath and electrode boundaries. Furthermore, once the required voltage-specific initial profiles are available, the trained network can rapidly reconstruct the current–voltage characteristic curve without rerunning the full fluid solver for each voltage case. These results demonstrate that the proposed PI-DeepONet can serve as an efficient surrogate for the prescribed one-dimensional fluid model, enabling rapid voltage-parameter scans while preserving consistency with the governing equations used in the present model.

Plasma simulation can effectively capture experimental observations in atmospheric radio-frequency (RF) plasmas. Nevertheless, it still poses a challenge to control and optimize experimental processes directly using simulation data. In this study, the Physics-Informed Neural Networks (PINNs) approach is proposed to solve inverse problems, in which the governing equations and observed data are incorporated as constraints into the loss function, and the key physical parameters, such as the electron mobility, are treated as a trainable parameter to be inferred. Based on the given data, the method leverages PINNs to inversely estimate a refined value of electron mobility, and the influence of data location and noise levels on the accuracy of the inferred parameter is also discussed. Since diagnostic data generally contain measurement errors and noise, and computational models are typically based on certain assumptions and approximations, the proposed PINNs framework provides a preliminary and model-consistent way to fine-tune critical parameters in numerical models by integrating physical constraints with sparse observational data. This proof-of-concept study offers a basis for future extensions toward experimental diagnostics and model-driven analysis of atmospheric RF plasmas.

Doxorubicin (DOX) is widely used in clinical chemotherapy, but its susceptibility to oxidation during the combined treatment with cold atmospheric plasma (CAP) raises concerns regarding its therapeutic efficacy. To improve drug stability and targeted delivery efficiency, this study employed classical molecular dynamics simulations to systematically investigate the mechanisms by which CAP-generated active particles and electric fields influence DOX encapsulation by carbon nanotubes (CNTs) and their transmembrane transport. Within a specific range of active particle concentrations, DOX aggregation is suppressed, enabling its spontaneous entry into CNTs for encapsulation. The CAP-induced electric field further promotes the directional migration of DOX, and once a threshold field strength is reached, the encapsulation efficiency is significantly enhanced. Moreover, an appropriate concentration of active particles can lower this threshold, enabling high encapsulation efficiency at electric field strengths as low as 0.3 V/nm. The introduction of CNTs can reduce the exposure of DOX to active particles, thereby effectively protecting it from CAP-induced oxidation. Regarding transmembrane transport, CAP-induced lipid oxidation decreases membrane structural stability, facilitating the intracellular internalization of CNTs and promoting the release of DOX within target cells. Furthermore, under the combined effects of oxidation and electric fields, the pulling force required for CNT transmembrane transport further decreases, the size of transmembrane pores increases, and the transmembrane delivery of DOX is enhanced. These results demonstrate that, under plasma synergy, CNTs exhibit significant potential in enhancing the targeted delivery of chemotherapeutic agents. This work provides important theoretical support for the application of plasma in targeted cancer therapy and offers new insights for the design of precision cancer treatment strategies.

With the requirement of intelligent control of atmospheric plasmas system, more Artificial Intelligence (AI) algorithms should be introduced into the field of plasma simulation. In this study, an innovative Physics-Informed Neural Networks (PINNs) framework for solving both forward and inverse problems in atmospheric radio frequency (RF) plasma is explored. For forward problems, the PINNs architecture with multi-scale feature is established by coupling Poisson’s equation, continuity equations, and drift-diffusion approximation equations into the loss function, to successfully capture the key plasma characteristics, showing good agreement with results from fluid simulation by discretization methods. For inverse problems that usually cannot be solved by discretization methods, by incorporating additional simulated (or measured) data of electric field as constraints in loss function, PINNs can accurately infer the applied voltage with relative errors smaller than 1%. The influence of various sampling positions, number of sampling points, and noise on the inversion of discharge parameter by PINNs is also investigated. In this study, according to the computational data, this mesh-less approach of PINNs successfully solves the fluid equations without relying on discretization methods and also shows the ability to inverse the discharge parameters such as applied voltage, driving frequency, or electrode spacing, given the data of electric field or plasma density, offering novel methodologies and insights for the intelligent control of atmospheric plasma systems.

The combined application of cold atmospheric plasma (CAP) and melittin (MEL) has emerged as a promising anticancer strategy. However, the molecular basis underlying their synergy remains unclear. Sialic acid, which is typically overexpressed on cancer cell membranes, modulates surface charge and oxidative sensitivity. Sialic acid also provides binding sites for cationic peptides, positioning it as a key mediator of CAP–MEL synergy. Here, we established a plasma–sialic acid–MEL simulation model to investigate how CAP-induced electric fields, the MEL concentration, and plasma-generated reactive oxygen and nitrogen species (RONS) collectively shape membrane permeability, energy barriers, and pore dynamics. The simulations revealed that sialic acid modification increases the electrostatic affinity of MEL for the membrane, thereby promoting its translocation and disruption under the influence of a nanosecond pulsed field (0.326 V/nm). Concurrently, RONS-induced lipid oxidation reduced membrane stability and amplified electro-molecular coupling. The extent of this synergy depends on the peptide-to-lipid ratio (P/L). For P/L = 4/128, sialic acid-enriched membranes exhibited notable permeability and RONS influx levels, whereas healthy-cell models experienced only fleeting perturbations. Overall, our findings reveal a threefold synergistic mechanism at the membrane level: sialic acid mediates selective targeting, the electric field regulates transmembrane energy barriers and drives field-dependent transport, and the melittin dosage dictates membrane permeability and the magnitude of structural disruption. In addition, RONS-induced lipid oxidation further amplifies this selective membrane-damaging effect. This multiscale coupling provides fresh molecular insight for designing plasma-assisted, peptide-based anticancer therapies that combine high precision with minimal collateral damage.

For underwater pulsed discharges, injecting an air bubble between electrodes serves as an effective external stimulation method. The bubble can significantly influence electrical parameters, sound waves, and pulsating cavities during the arc discharge and subsequent stages. Investigating the influence and mechanisms of the bubble in a single discharge event is essential for the optimized control of underwater pulsed discharges. This study utilized the schlieren optical system to record the morphology and evolution of sound waves and pulsating cavities, while hydrophones measured sound-wave intensity. A computational model based on the energy balance equation and the Gilmore model was used to describe the dynamic evolution of pulsating cavities. The results indicate that the presence of the bubble significantly alters the morphology of sound waves and the pulsating cavity, inducing transient stratification within the pulsating cavity. Furthermore, it demonstrates notable advantages in enhancing sound-wave intensity, increasing internal energy of the pulsating cavity, and improving energy conversion efficiency. The bubble guides and distorts the discharge channel, and there is an optimal bubble position that significantly enhances sound-wave intensity. The dynamic characteristics of the pulsating cavity are influenced by energy parameters and external constraints, and these parameters primarily affect the initial stage of the cavity. This study has important theoretical implications for the efficient application of underwater pulsed discharges in practical production scenarios.

Underwater pulsed spark discharge (UPSD) shock wave focusing is widely used in marine exploration and lithotripsy. However, research on partial elliptical reflectors within restricted space remains limited. This study developed a fluid–structure interaction model using multi-material arbitrary Lagrangian–Eulerian that had been experimentally validated, substituting the arc expansion process generated by spark discharge with an explosive detonation. We compared the effects of spherical and cylindrical explosion sources with different aspect ratios on shock wave propagation, focusing gain, and energy transmission. We analyzed the axial and radial shock wave behaviors of cylindrical explosion sources and assessed their energy conversion efficiency. The results showed that spherical explosion sources had the highest energy conversion efficiency, whereas the transfer efficiency of cylindrical explosion sources decreased with increasing aspect ratios. The paper concludes with an explanation of the directional characteristics of energy propagation from cylindrical explosive sources based on observed energy change trends in water and the elliptical focusing reflector.

The increasing presence of chemotherapeutic drug residues in aquatic environments poses serious challenges due to their poor biodegradability and toxicity. Methotrexate (MTX), a widely used anti-cancer drug, is largely excreted from the body unmetabolized and ends up in wastewater, often untreated by traditional treatment methods. Advanced oxidation processes have been investigated for MTX removal, with non-thermal plasma (NTP) emerging as a promising, sustainable alternative. Experimental studies using a pencil plasma jet have demonstrated substantial degradation and mineralization of MTX, yet the molecular-level pathways remain insufficiently understood. In this study, we employ reactive molecular dynamics simulations to examine oxidative degradation of MTX by NTP-generated oxygen atoms, a key reactive oxygen species. The simulations reveal hydroxylation, ring opening, fragmentation, and CO ₂ release as dominant mechanisms, showing how oxygen radicals progressively destabilize and decompose MTX. These findings are qualitatively consistent with experimental observations and highlight how integrating computational and experimental approaches deepens mechanistic understanding of plasma-induced pharmaceutical degradation, supporting the development of optimized wastewater treatment strategies.

Plasma medicine as an emerging field is gradually demonstrating its great potential in sterilization, wound healing, cancer treatment, and promoting tissue regeneration. The core lies in the complex interactions between various reactive species produced by cold atmospheric plasma (CAP) and biological bodies, thereby triggering a series of biological effects. However, the underpinning mechanisms of these effects have not been fully elucidated, limiting the widespread application and optimization in clinical practice. This study delves into the interaction mechanism between nucleotides and reactive oxygen species (ROS) through reactive molecular dynamics (MD) simulations. The simulation results indicate that the reactions typically commence with H‐abstraction, followed by a series of oxidation reactions, including single and double bond transformations, dehydrogenation, hydroxylation, alcohol formation, aldehyde formation, among others. Moreover, for long DNA chains, various forms of strand breaks are observed, suggesting that CAP can deconstruct DNA structure, potentially leading to damage to cellular structures. The dose‐effect analysis was also performed by varying the number of ROS in the simulation box, providing quantitative evidence for the explanation of oxidative damage. The calculated results were represented using a more concise SMILES notation, thereby facilitating the storage and sharing of reaction mechanisms and final products, which enhances the applicability and utility of these numerical data.