Continuous monitoring of physiological pH is essential for disease diagnosis and health management, yet developing minimally invasive implantable sensors with adequate stability remains challenging. We developed a micro-electrochemical pH sensor fabricated on a flexible gold wire substrate modified with a platinum black and polyaniline nanocomposite. Through a two-step electrochemical deposition process, a porous platinum nanostructure was constructed to increase the electrochemically active surface area, providing a stable scaffold for the subsequent polyaniline coating. The resulting sensor displayed a linear response from pH 4 to 10 with a sensitivity of −45.2 mV/pH (R2 = 0.998) and intra-batch reproducibility with a coefficient of variation below 1.4%. Specificity tests confirmed negligible potential drift in the presence of common interfering ions. The sensor's practical applicability was validated in complex biological environments; in diluted serum, it maintained a stable baseline and responded rapidly to pH fluctuations, effectively resisting biofouling. In vivo experiments involving subcutaneous implantation in an anesthetized mouse successfully tracked continuous pH variations induced by chemical stimuli. After seven days in phosphate-buffered saline, the sensor retained 78.5% of its initial response. This work presents a robust, easily fabricated microsensor strategy promising for continuous in vivo monitoring of pH.
Halide perovskites have attracted significant interest due to their potential in optoelectronic devices. However, challenges related to complex compositional spaces, environmental sensitivity, and stability limitations continue to constrain their systematic development and application. Machine learning (ML) has emerged as an effective tool to address these challenges by enabling the prediction of material properties, the identification of promising compositions, and optimization of processing conditions, while reducing reliance on conventional trial‐and‐error methods. By capturing complex, nonlinear relationships among compositional, structural, and processing parameters, ML enables the exploration of broad design spaces that are essential for advancing perovskite research. Additionally, ML accelerates the discovery and optimization of perovskite materials through data‐driven approaches, including high‐throughput screening and inverse design, enabling rapid identification of optimal compositions and processing conditions for enhanced device performance and stability. This review provides an overview of recent efforts to integrate ML into halide perovskite studies, discussing workflows, implementation strategies, and notable progress in device‐level development. This article highlights how ML enables systematic materials discovery and optimization, supporting the advancement of stable and efficient perovskite optoelectronic devices.
This study employs a screen-printed aluminum paste to form electrode patterns on a substrate. Subsequently, a galvanic displacement reaction is utilized to replace the surface of the printed aluminum electrode with a copper seed layer. Finally, copper electroplating is performed to deposit copper onto the seed layer. This innovative additive aluminum-displacement-plus-electroplating process utilizes upward electroplating from the displacement-formed copper seed layer to enhance the electrical properties and smoothness of the copper conductor, while the downward electroplating strengthens the mechanical properties of the copper conductors.To ensure the complete conversion of the printed aluminum electrodes into copper electrodes, crystalline copper sulfate powder is incorporated into the aluminum paste. After 20 min of displacement followed by 30 min of electroplating, the internal aluminum is fully converted into copper, producing a copper layer approximately 100 μm thick. The resulting copper exhibits a resistivity of 2 × 10−8 Ω·m.Compared with conventional subtractive copper foil conductor processes, this method achieves comparable mechanical, chemical, and electrical properties. Furthermore, the simplified process reduces material waste and supports the pursuit of net-zero carbon emissions.
Anees Al-Najjar, Nageswara S. V. Rao, Craig A. Bridges
et al.
Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem's wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.
Proton-coupled electron transfers (PCET) are elementary steps in electrocatalysis. However, accurate calculations of PCET rates remain challenging, especially considering nuclear quantum effects (NQEs) under a constant potential condition. Statistical sampling of reaction paths is an ideal approach for rate calculations, however, is always limited by the rare-event issue. Here we develop an electrochemistry-driven quantum dynamics approach enabling realistic enhanced paths sampling under constant potentials without a priori defined reaction coordinates. We apply the method in modeling the Volmer step of the hydrogen evolution reaction, and demonstrate that the NQEs exhibit more than one order of magnitude impact on the computed rate constant, indicating an essential role of NQEs in electrochemistry.
This paper presents the full dynamic model of the UR10 industrial robot. A triple-stage identification approach is adopted to estimate the manipulator's dynamic coefficients. First, linear parameters are computed using a standard linear regression algorithm. Subsequently, nonlinear friction parameters are estimated according to a sigmoidal model. Lastly, motor drive gains are devised to map estimated joint currents to torques. The overall identified model can be used for both control and planning purposes, as the accompanied ROS2 software can be easily reconfigured to account for a generic payload. The estimated robot model is experimentally validated against a set of exciting trajectories and compared to the state-of-the-art model for the same manipulator, achieving higher current prediction accuracy (up to a factor of 4.43) and more precise motor gains. The related software is available at https://codeocean.com/capsule/8515919/tree/v2.
Image-based visual servoing (IBVS) methods have been well developed and used in many applications, especially in pose (position and orientation) alignment. However, most research papers focused on developing control solutions when 3D point features can be detected inside the field of view. This work proposes an innovative feedforward-feedback adaptive control algorithm structure with the Youla Parameterization method. A designed feature estimation loop ensures stable and fast motion control when point features are outside the field of view. As 3D point features move inside the field of view, the IBVS feedback loop preserves the precision of the pose at the end of the control period. Also, an adaptive controller is developed in the feedback loop to stabilize the system in the entire range of operations. The nonlinear camera and robot manipulator model is linearized and decoupled online by an adaptive algorithm. The adaptive controller is then computed based on the linearized model evaluated at current linearized point. The proposed solution is robust and easy to implement in different industrial robotic systems. Various scenarios are used in simulations to validate the effectiveness and robust performance of the proposed controller.
Mayara Masae Kubota, Paulo Rogério Catarini da Silva, Henrique de Santana
To study how the presence of [6,6]-phenyl-C₆₁-butyric acid methyl ester (PCBM) affects interactions and electronic transfer between poly(3-alkylthiophenes) (P3ATs) and titanium dioxide (TiO2), which are priority effects in the active layer of inverted organic solar cells, PCBM was first incorporated into the ITO/TiO2 system and then the poly(3-methylthiophene) or poly(3-hexylthiophene) films were electrochemically deposited. The interfaces formed were called ITO/TiO2/PCBM/P3ATs, subsequently characterized by Raman spectroscopy. The presence of PCBM was found to destabilize the radical cation segments of P3ATs, favoring the dication segments and interfering with the strong interaction observed between P3ATs and TiO2 studied in a previous work. The possibility of interaction between PCBM and TiO2 was also studied using the X-ray Diffraction technique, but demonstrated the non-interference of PCBM in the crystalline structure of TiO2. These results were supplemented by Confocal Raman images, presenting areas of higher and lower concentrations of PCBM, TiO2 and P3ATs. However, even with these areas, the spectra demonstrated a certain homogeneity of the segments studied throughout the generated film.
Salim Hussain, Adeniyi Oyebade, Md Riyad Hossain
et al.
The demand for effective, economical, and sustainable anode materials for metal-ion batteries (MIBs) has increased significantly due to the rapid growth of energy storage technologies. Among various candidates, carbon-based materials have emerged as highly promising due to their abundance, structural versatility, and favorable electrochemical properties. This review highlights the current status and future directions of carbon-based anode materials in MIBs, with a particular focus on graphite, hard carbon, carbon nanotubes, heteroatom-doped carbons, carbon-based composites, and other related structures. Various synthesis strategies for these materials are presented, along with discussions on their physicochemical characteristics, including structural features that influence electrochemical performance. Furthermore, we provided an overview on the performance of newly developed carbon-based anode materials in lithium-, sodium-, potassium-, and other emerging metal-ion battery systems to assess the impact of different synthesis approaches. Special attention is given to surface engineering, heteroatom doping, and composite design that can address intrinsic challenges such as limited ion diffusion, low reversible capacity, and poor cycling stability in MIBs. This review does not cover any carbon materials which have been used as an additive. In addition, the review explores emerging opportunities enabled by advanced characterization techniques, computational modeling, and artificial intelligence for optimizing the design of next-generation carbon anode. Finally, this article provides future perspectives and insights into the design principles of novel carbon-based anode materials that can accelerate the development of high-performance, durable, and sustainable MIB technologies.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Hexagonal boron nitride (h‐BN), with its unique structural and thermal properties, has emerged as a versatile material capable of addressing challenges such as thermal instability, dendrite formation, and limited ionic conductivity across liquid, gel polymer, and solid‐state electrolytes (SSEs) for high‐performing lithium ion and lithium metal batteries (LMBs). In liquid electrolytes, h‐BN improves ionic mobility and suppresses side reactions, while in gel polymer electrolytes (GPEs), it enhances mechanical flexibility and thermal stability. SSEs benefit from h‐BN's ability to suppress dendrites, reinforce mechanical strength, and optimize interfacial compatibility, making it a key enabler for next‐generation battery technologies. Despite its promise, challenges such as dispersion uniformity, cost, and interfacial complexity must be addressed. Future directions, including the development of multifunctional architectures, dynamic electrolytes, and sustainable synthesis methods, are discussed to guide the integration of h‐BN in emerging energy storage systems. This perspective article explores the multifunctional roles of h‐BN, highlighting its contributions to enhancing ionic transport, thermal management, and interfacial stability. By presenting a comprehensive overview of h‐BN's role in electrolytes, this work aims to inspire further research into its potential to revolutionize energy storage technologies.
Recently, 6G in-X subnetworks have been proposed as low-power short-range radio cells to support localized extreme wireless connectivity inside entities such as industrial robots, vehicles, and the human body. Deployment of in-X subnetworks within these entities may result in rapid changes in interference levels and thus, varying link quality. This paper investigates distributed dynamic channel allocation to mitigate inter-subnetwork interference in dense in-factory deployments of 6G in-X subnetworks. This paper introduces two new techniques, Federated Multi-Agent Double Deep Q-Network (F-MADDQN) and Federated Multi-Agent Deep Proximal Policy Optimization (F-MADPPO), for channel allocation in 6G in-X subnetworks. These techniques are based on a client-to-server horizontal federated reinforcement learning framework. The methods require sharing only local model weights with a centralized gNB for federated aggregation thereby preserving local data privacy and security. Simulations were conducted using a practical indoor factory environment proposed by 5G-ACIA and 3GPP models for in-factory environments. The results showed that the proposed methods achieved slightly better performance than baseline schemes with significantly reduced signaling overhead compared to the baseline solutions. The schemes also showed better robustness and generalization ability to changes in deployment densities and propagation parameters.
In fire surveillance, Industrial Internet of Things (IIoT) devices require transmitting large monitoring data frequently, which leads to huge consumption of spectrum resources. Hence, we propose an Industrial Edge Semantic Network (IESN) to allow IIoT devices to send warnings through Semantic communication (SC). Thus, we should consider (1) Data privacy and security. (2) SC model adaptation for heterogeneous devices. (3) Explainability of semantics. Therefore, first, we present an eXplainable Semantic Federated Learning (XSFL) to train the SC model, thus ensuring data privacy and security. Then, we present an Adaptive Client Training (ACT) strategy to provide a specific SC model for each device according to its Fisher information matrix, thus overcoming the heterogeneity. Next, an Explainable SC (ESC) mechanism is designed, which introduces a leakyReLU-based activation mapping to explain the relationship between the extracted semantics and monitoring data. Finally, simulation results demonstrate the effectiveness of XSFL.
Effective information gathering and knowledge codification are pivotal for developing recommendation systems that promote circular economy practices. One promising approach involves the creation of a centralized knowledge repository cataloguing historical waste-to-resource transactions, which subsequently enables the generation of recommendations based on past successes. However, a significant barrier to constructing such a knowledge repository lies in the absence of a universally standardized framework for representing business activities across disparate geographical regions. To address this challenge, this paper leverages Large Language Models (LLMs) to classify textual data describing economic activities into the International Standard Industrial Classification (ISIC), a globally recognized economic activity classification framework. This approach enables any economic activity descriptions provided by businesses worldwide to be categorized into the unified ISIC standard, facilitating the creation of a centralized knowledge repository. Our approach achieves a 95% accuracy rate on a 182-label test dataset with fine-tuned GPT-2 model. This research contributes to the global endeavour of fostering sustainable circular economy practices by providing a standardized foundation for knowledge codification and recommendation systems deployable across regions.
Adrián Pérez-Resa, Miguel García-Bosque, Carlos Sánchez-Azqueta
et al.
Industrial Ethernet is a technology widely spread in factory floors and critical infrastructures where a high amount of data need to be collected and transported. Fiber optic networks at gigabit rates fit well with that type of environments where speed, system performance and reliability are critical. In this work a new encryption method for high speed optical communications suitable for such kind of networks is proposed. This new encryption method consists of a symmetric streaming encryption of the 8b/10b data flow at PCS (Physical Coding Sublayer) level. It is carried out thanks to an FPE (Format Preserving Encryption) blockcipher working in CTR (Counter) mode. The overall system has been simulated and implemented in an FPGA (Field Programmable Gate Array). Thanks to experimental results it can be concluded that it is possible to cipher traffic at this physical level in a secure way. In addition, no overhead is introduced during encryption, getting minimum latency and maximum throughput.
In this work, we study the influence of cation concentration and identity on the hydrogen evolution reaction (HER) on polycrystalline platinum (Pt) electrode in pH 3 electrolytes. Our observations indicate that cations in the electrolyte do not affect proton reduction at low potentials. However, an increase in cation concentration significantly enhances water reduction. Simultaneously, we identify a non-negligible migration current under mass transport limited conditions in electrolytes with low cation concentration. To separate migration effects from specific cation-promotion effects on HER, we carried out further experiments with electrolytes with mixtures of Li+ and K+ cations. Our results show that, adding strongly hydrated cations (Li+) to a K+-containing electrolyte leads to a less negative onset potential of water reduction. Interfacial pH measurements reveal a same interfacial pH at the platinum electrode in pH 3 in the presence of 80 mM LiClO4 and KClO4, respectively, at potentials where water reduction occurs. Based on these results, we suggest that under the current conditions, the strongly hydrated cations (Li+) promote water dissociation on the Pt electrode more favorably in comparison with the more weakly hydrated cations (K+), and that this promotion is not related to a local pH effect.
Antimony sulfide (Sb2S3) has garnered significant attention recently due to its remarkable photovoltaic properties and low toxicity. However, the conventional physical vapor deposition approach faces challenges in achieving high-quality films due to Sb2S3 having a quasi-one-dimensional nanoribbon structure. In contrast, solution-processed Sb2S3 thin films have shown improved photovoltaic behavior, offering a low-cost and scalable fabrication method. Nonetheless, the sensitivity of the solution process to the chemical composition of the precursor poses a challenge, often requiring noble gas protection to prevent exposure to toxic solvents or moisture-sensitive chemicals. Despite this, the impact of precursor fabrication conditions on film growth behavior remains unexplored. In our study, we investigate how different processing atmospheres of precursors, namely nitrogen (N2) and air, affect grain growth and the associated optical and electronic performance of Sb2S3 thin films. Our findings reveal that the presence of oxygen in the precursor can hinder grain growth by obstructing surface integration sites, resulting in undesired (hk0) orientation and even the formation of Sb2O3 on the surface of the Sb2S3 films, despite identical post-deposition conditions. This research sheds light on how the ambient conditions during precursor preparation can influence grain engineering, thereby providing valuable insights for controlling the grain size and producing high-quality Sb2S3 absorber films.
Tiago Afonso Salgueiro, Rita Carvalho Veloso, João Ventura
et al.
The global environmental crisis necessitates reliable, sustainable, and safe energy storage solutions. The current systems are nearing their capacity limits due to the reliance on conventional liquid electrolytes, which are fraught with stability and safety concerns, prompting the exploration of solid-state electrolytes, which enable the integration of metal electrodes. Solid-state sodium-ion batteries emerge as an appealing option by leveraging the abundance, low cost, and sustainability of sodium. However, low ionic conductivity and high interfacial resistance currently prevent their widespread adoption. This study explores polyvinyl-based polymers as wetting agents for the NASICON-type NZSP (Na<sub>3</sub>Zr<sub>2</sub>Si<sub>2</sub>PO<sub>12</sub>) solid electrolyte, resulting in a combined system with enhanced ionic conductivity suitable for Na-ion solid-state full cells. Electrochemical impedance spectroscopy (EIS) performed on symmetric cells employing NZSP paired with different wetting agent compositions demonstrates a significant reduction in interfacial resistance with the use of poly(vinyl acetate)—(PVAc-) based polymers, achieving an impressive ionic conductivity of 1.31 mS cm<sup>−1</sup> at room temperature, 63.8% higher than the pristine material, notably reaching 7.36 mS cm<sup>−1</sup> at 90 °C. These results offer valuable insights into the potential of PVAc-based polymers for advancing high-performance solid-state sodium-ion batteries by reducing their total internal resistance.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Recently, the selection of machine learning model based on only the data distribution without concerning the noise of the data. This study aims to distinguish, which models perform well under noisy data, and establish whether stacking machine learning models actually provide robustness to otherwise weak-to-noise models. The electrochemical data were tested with 12 standalone models and stacking model. This includes XGB, LGBM, RF, GB, ADA, NN, ELAS, LASS, RIDGE, SVM, KNN, DT, and the stacking model. It is found that linear models handle noise well with the average error of (slope) to 1.75 F g-1 up to error per 100% percent noise added; but it suffers from prediction accuracy due to having an average of 60.19 F g-1 estimated at minimal error at 0% noise added. Tree-based models fail in terms of noise handling (average slope is 55.24 F g-1 at 100% percent noise), but it can provide higher prediction accuracy (lowest error of 23.9 F g-1) than that of linear. To address the controversial between prediction accuracy and error handling, the stacking model was constructed, which is not only show high accuracy (intercept of 25.03 F g-1), but it also exhibits good noise handling (slope of 43.58 F g-1), making stacking models a relatively low risk and viable choice for beginner and experienced machine learning research in electrochemistry. Even though neural networks (NN) are gaining popularity in the electrochemistry field. However, this study presents that NN is not suitable for electrochemical data, and improper tuning resulting in a model that is susceptible to noise. Thus, STACK models should provide better benefits in that even with untuned base models, they can achieve an accurate and noise-tolerant model. Overall, this work provides insight into machine learning model selection for electrochemical data, which should aid the understanding of data science in chemistry context.
Zheng Ma, Laura Fuentes-Rodriguez, Zhengwei Tan
et al.
Modulation of magnetic properties through voltage-driven ion motion and redox processes, i.e., magneto-ionics, is a unique approach to control magnetism with electric field for low-power memory and spintronic applications. So far, magneto-ionics has been achieved through direct electrical connections to the actuated material. Here we evidence that an alternative way to reach such control exists in a wireless manner. Induced polarization in the conducting material immersed in the electrolyte, without direct wire contact, promotes wireless bipolar electrochemistry, an alternative pathway to achieve voltage-driven control of magnetism based on the same electrochemical processes involved in direct-contact magneto-ionics. A significant tunability of magnetization is accomplished for cobalt nitride thin films, including transitions between paramagnetic and ferromagnetic states. Such effects can be either volatile or non-volatile depending on the electrochemical cell configuration. These results represent a fundamental breakthrough that may inspire future device designs for applications in bioelectronics, catalysis, neuromorphic computing, or wireless communications.