Real-time Process Monitoring Research Articles

Exploring Real-Time Monitoring of Adsorption Processes at Solid–Liquid Interfaces through Chemical Force Microscopy

Exploring Real-Time Monitoring of Adsorption Processes at Solid–Liquid Interfaces through Chemical Force Microscopy

Biodegradable and Implantable Triboelectric Nanogenerator Improved by β-Lactoglobulin Fibrils-Assisted Flexible PVA Porous Film.

Triboelectric nanogenerators (TENGs) are highly promising as implantable, degradable energy sources and self-powered sensors. However, the degradable triboelectric materials are often limited in terms of contact electrification and mechanical properties. Here, a bio-macromolecule-assisted toughening strategy for PVA aerogel-based triboelectric materials is proposed. By introducing β-lactoglobulin fibrils (BF) into the PVA aerogel network, the material's mechanical properties while preserving its swelling resistance is significantly enhanced. Compared to pure PVA porous film, the BF-PVA porous film exhibits an eightfold increase in fracture strength (from 1.92 to 15.48J) and a fourfold increase in flexibility (from 10.956 to 39.36MPa). Additionally, the electrical output of BF-PVA in triboelectric performance tests increased nearly fivefold (from 45 to 203V). Leveraging these enhanced properties, a biodegradable TENG (bi-TENG) for implantable muscle activity sensing is developed, achieving real-time monitoring of neuromuscular processes. This innovation holds significant potential for advancing implantable medical devices and promoting new applications in bio-integrated electronics.

Construction error control method of large-span spatial structures based on digital twin

In the process of construction of large-span spatial structures, there are many control elements, which need to carry on the dual control of internal force and shape. The traditional control method cannot measure the construction deviation completely and accurately, and there is a lag in information feedback. In this paper, spatio-temporal information is integrated into digital twin and a construction error control method is proposed for large-span spatial structures. By analyzing the fusion mechanism of spatio-temporal information and digital twin, a closed-loop control system for structural errors before, during and after construction is formed. Driven by the control architecture, the method of structure simulation analysis and real-time monitoring during construction is proposed, and the functional relationship between cable force and cable length is established. Based on point cloud data, construction error optimization detection method is proposed for structure formation state. The nonlinear relation between elevation change rate and cable force deviation is formed and the control measures of formation state error are obtained. The experimental model of cable truss structure is taken as an example to apply and verify the theoretical method. The construction error control twin platform is formed in the experiment. Driven by the platform, the twinning analysis and real-time monitoring of the construction process are carried out to control the error of the structure formation. The results show that this method integrates spatio-temporal information into digital twin to realize data integration processing. The optimized error detection method can save 30% time cost and effectively control the construction error within 3%. The integration of spatio-temporal information and digital twin provides theoretical and algorithm support for the intelligent management and control of large-span spatial structure construction.

AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of bioremediation by enabling real-time monitoring and optimization of environmental cleanup processes. This paper explores the integration of AI-driven monitoring systems with bioremediation techniques, focusing on how real-time data analysis and predictive modelling can enhance the effectiveness of pollutant degradation. These AI systems continuously collect data from contaminated sites, such as soil and water, and analyse variables like microbial activity, pollutant concentration, and environmental conditions. By processing this data, AI models can optimize the bioremediation environment, adjusting factors such as pH, temperature, and nutrient levels to maximize microbial efficiency. Predictive modelling plays a crucial role in forecasting remediation outcomes, allowing for proactive adjustments to improve the speed and success of the process. The study also highlights the potential of AI in reducing operational costs by automating data collection and analysis, minimizing the need for manual intervention. Furthermore, it discusses challenges related to data quality, system integration, and the scalability of AI applications in real-world bioremediation projects. By leveraging AI's capability to provide real-time insights and predictive analytics, this research demonstrates its potential to significantly enhance the precision and sustainability of bioremediation efforts, paving the way for smarter environmental management.

Kinetic analysis of the self-discharge of the NiOOH OER active phase in KOH electrolyte: insights from in-situ Raman and UV–Vis reflectance spectroscopies

NiOOH has been established as the active phase of NiO-based electrocatalysts in the alkaline Oxygen Evolution Reaction (OER). Here, we investigate the self-discharge behavior of NiOOH electrodes under open circuit potential (OCP) conditions in 1 M KOH electrolyte by monitoring phase changes via in-situ Raman and UV–Vis reflectance spectroscopies and performing kinetic analyses on the OCP and spectroscopic data. Our findings reveal a linear phase change from NiOOH to Ni(OH)2 over time, indicative of a 0th-order reduction reaction. Contrarily, the OCP evolution associated with this phase reduction displayed a combination of linear and exponential decay patterns as a result of various kinetics, including Faradaic processes and diffusion-controlled mechanisms, influencing the self-discharge potential over 1.25 V (vs RHE). An additional linear region at lower potentials (<1.25 V (vs RHE)) suggests that charge redistribution due to the phase change from α-Ni(OH)2 to β-Ni(OH)2 dominates the self-discharge, a behavior confirmed by in-situ UV–Vis reflectance spectroscopy. These findings highlight the effectiveness of combining in-situ Raman and UV–Vis spectroscopy with electrochemical data for real-time monitoring of electrochemical processes, here potential-dependent electrocatalyst phase changes, leading to a more detailed and accurate understanding of the dynamic behavior, phase change kinetics, and self-discharge behaviors of solid electrocatalysts that can guide the design of more efficient and durable energy storage and conversion materials.

Phototriggered Hydrogen Persulfide Donors via Hydrosulfide Radical Formation Enhancing the Reactive Sulfur Metabolome in Cells.

Hydrogen persulfide (H2S2) is an important sulfur-containing signaling molecule that plays a crucial role in the homeostasis of various organ systems, such as the renal, cardiovascular, liver, and gastrointestinal systems. However, research on H2S2 in biological settings is still challenging due to its instability and high reactivity. Compounds that can controllably release H2S2 (also known as donors) are thus crucial research tools. Currently, available H2S2 donors are still very limited, with most of them relying on modified disulfide templates. These templates possess an unavoidable limitation of being susceptible to cellular disulfide exchange which can compromise their efficacy. In this work, we explored nondisulfide-based and nonoxidation-dependent templates for the design of H2S2 donors. We found that tertiary naphthacyl thiols could undergo phototriggered C-S homolytic cleavage to form H2S2 via hydrosulfide (HS) radicals. In addition, the release of H2S2 was associated with the formation of a product with strong blue fluorescence, which allowed for real-time monitoring of the release process. This reaction was demonstrated to proceed effectively in both buffers and cells, with the ability to enhance intracellular production of persulfides, including GSSH, CysSSH, H2S2, H2S3, etc. It provides a unique photocontrolled H2S2 donor system with distinct advantages compared to known H2S2 donors due to its good stability and spatiotemporal control ability.

Progress and challenges in structural, in situ and operando characterization of single-atom catalysts by X-ray based synchrotron radiation techniques.

Single-atom catalysts (SACs) represent the ultimate size limit of nanoscale catalysts, combining the advantages of homogeneous and heterogeneous catalysts. SACs have isolated single-atom active sites that exhibit high atomic utilization efficiency, unique catalytic activity, and selectivity. Over the past few decades, synchrotron radiation techniques have played a crucial role in studying single-atom catalysis by identifying catalyst structures and enabling the understanding of reaction mechanisms. The profound comprehension of spectroscopic techniques and characteristics pertaining to SACs is important for exploring their catalytic activity origins and devising high-performance and stable SACs for industrial applications. In this review, we provide a comprehensive overview of the recent advances in X-ray based synchrotron radiation techniques for structural characterization and in situ/operando observation of SACs under reaction conditions. We emphasize the correlation between spectral fine features and structural characteristics of SACs, along with their analytical limitations. The development of IMST with spatial and temporal resolution is also discussed along with their significance in revealing the structural characteristics and reaction mechanisms of SACs. Additionally, this review explores the study of active center states using spectral fine characteristics combined with theoretical simulations, as well as spectroscopic analysis strategies utilizing machine learning methods to address challenges posed by atomic distribution inhomogeneity in SACs while envisaging potential applications integrating artificial intelligence seamlessly with experiments for real-time monitoring of single-atom catalytic processes.

Efficient Aerial Water Harvesting with Self-Sensing Dynamic Janus Crystals.

Water scarcity is one of the most pressing issues of contemporary societal development that requires innovative technologies where the material not only harvests water but also plays an active role in the process. Here, we demonstrate a highly efficient optical self-sensing approach to humidity capture from the air, where both humidity-harvesting and water-transduction functionalities are imparted on slender organic crystals by partial silanization via layer-by-layer hybridization. We report that due to the integration of the harvesting of aerial moisture and the collection of the condensed water, the ensuing Janus-type crystals capture humidity with the highest-to-date water collection efficiency of 15.96 ± 0.63 g cm-2 h-1. The water-collecting elements are also capable of delivering the water by reversible and periodic elastic deformation, and their high optical transparency allows real-time monitoring of the periodic fog collection process by deformational modulation of passively or actively transduced light that outcouples at the crystal-droplet interface. The results could inspire sophisticated approaches to humidity harvesting where optically transparent crystals combine fog capture with self-sensing capabilities for continuous and optimized operation to maximize the cost-gain balance of aerial fog capture.

Tumor microenvironment-based smart beacon drug delivery system for in situ visualization of tumor therapy

Real-time monitoring of the effectiveness of tumor-targeted drugs is crucial for assessing their impact. To address this need, we developed a beacon drug delivery system, MDB, which incorporates tumor targeting, anti-tumor effects, and near-infrared fluorescence emission for on-site visualization of tumor therapy. In non-cancerous cells, MDB remains inactive as a prodrug but is selectively activated within tumor cells when the borate ester group reacts with H2O2 in the tumor microenvironment (TME), releasing the beacon drug MDBRe. MDBRe can down-regulate the expression of the protein heme oxygenase 1 (HO-1), ultimately disrupting the mitochondrial energy supply system and leading to tumor cell death. It also emits near-infrared fluorescence for drug tracking under structured illumination microscopy (SIM). Both in vitro and in vivo experiments confirm the effective delivery of MDBRe by MDB to H2O2-enriched tumor tissues, resulting in a potent anti-tumor effect, visualization of drug distribution in tumors, and monitoring of therapy progress. In conclusion, our approach presents a novel method for targeted drug delivery to tumors and enables real-time visual monitoring of the treatment process.

Quantitative evaluation of anti-biofilm cavitation activity seeded from microbubbles or protein cavitation nuclei by passive acoustic mapping

Bacterial biofilms represent a major challenge for effective antibiotic therapy as they confer physical and functional changes that protect bacteria from their surrounding environment. In this work, focused ultrasound in combination with cavitation nuclei was used to disrupt biofilms of Staphylococcus aureus and Pseudomonas aeruginosa, both of which are on the World Health Organization's priority list for new antimicrobial research. &#xD;Approach: Single species biofilms were exposed to ultrasound (0.5 MHz centre frequency, 0.5-1.5 MPa peak rarefactional pressure, 200 cycle pulses, 5 Hz repetition frequency, 30 s duration), in the presence of two different types of cavitation nuclei. Quantitative passive acoustic mapping (PAM) was used to monitor cavitation emissions during treatment using a calibrated linear array. &#xD;Main Results: It was observed that the cumulative energy of acoustic emissions during treatment was positively correlated with biofilm disruption, with differences between bacterial species attributed to differences in biofilm morphology. PCaN provided increased biofilm reduction compared to microbubbles due in large part to their persistence over the duration of ultrasound exposure. There was also good correlation between the spatial distribution of cavitation as characterized by PAM and the extent of biofilm disruption observed with microscopy. &#xD;Significance: Collectively, the results from this work indicate the potential broad applicability of cavitation for eliminating biofilms of priority pathogens and the opportunity presented by Passive Acoustic Mapping for real-time monitoring of antimicrobial processes.

Endothelium-targeted NF-κB siRNA nanogel for magnetic resonance imaging and visualized-anti-inflammation treatment of atherosclerosis

Atherosclerosis-induced lethal cardiovascular disease remains a severe healthcare threat due to the limited drug efficiency and untimely prediction of high-risk events caused by inadequate target specificity of medications, incapable recognition of insensitive patients, and variable morphology of vulnerable plaques. Therefore, it is necessary to develop efficient strategies to improve the diagnosis accuracy and achieve visualized treatment of atherosclerosis. Herein, we establish an inflamed endothelium-targeted three-in-one nucleic acid nanogel system that can reverse the inflammatory state of endothelial cells (ECs) in plaques and simultaneously achieve real-time monitoring of the therapy process for efficient atherosclerosis diagnosis and treatment. For this purpose, contrast agent (Gd-DOTA) and VCAM-1-targeted peptide (VP) are first covalently conjugated onto DNA strands by click reaction respectively, which could self-assemble into Y-shaped structures (Gd-Y1 and VP-Y2 motifs) with magnetic resonance (MR) imaging and endothelium targeting capacities. Thereafter, NF-κB subunit p65-targeting siRNA (siNF-κB) is crosslinked with Gd-Y1 and VP-Y2 motifs to construct the endothelium-targeting nanogel platform. With contrast agents inside, the nanogel enables MR-based diagnosis and visualized therapy of atherosclerosis, providing accurate prognostic analysis and indications for treatment results, which ensures timely disclosure of insensitive individuals and avoids acute lethal events. By delivering siNF-κB to inflammatory endothelium, the nanogel significantly regresses plaques in both the aorta and carotid artery with reduced inflammation cytokines, collagens, macrophages, and apoptotic cells, providing a potential anti-inflammation strategy to treat atherosclerosis and avoid acute cardiovascular disease.

Advanced AI Technologies for Defect Prevention and Yield Optimization in PCB Manufacturing

The integration of Advanced Artificial Intelligence (AI) technologies in Printed Circuit Board (PCB) manufacturing has revolutionized the industry, offering significant improvements in defect prevention and yield optimization. As PCB manufacturing processes become increasingly complex and intricate, traditional methods of defect detection and quality assurance are often insufficient to meet the high demand for precision and efficiency. This paper explores the application of cutting-edge AI methodologies, including machine learning, computer vision, and neural networks, to address critical challenges in PCB production. The primary focus of this research is on the role of AI in enhancing automated defect detection systems and optimizing production yield. AI-based systems such as predictive maintenance, real-time process monitoring, and intelligent decision-making have been shown to drastically reduce defects while improving overall production throughput. Machine learning algorithms can identify subtle defects in real time, often undetectable by conventional methods, while neural networks can analyze historical data to predict potential failures before they occur. Additionally, AI-driven optimization techniques help manufacturers adjust production parameters dynamically, ensuring higher yields and minimizing waste. Through a combination of theoretical analysis and case studies, this paper highlights the effectiveness of AI-driven solutions in PCB manufacturing. Results from industry applications indicate significant improvements in both quality control and yield rates, providing a competitive edge to manufacturers adopting these technologies. Furthermore, this paper discusses future trends, including the integration of AI with the Internet of Things (IoT), edge computing, and sustainable manufacturing practices, which will further enhance the capabilities of AI systems in this field. The findings suggest that advanced AI technologies are not only capable of overcoming existing challenges in defect prevention but also hold the potential to reshape the future of PCB manufacturing by offering highly adaptive, precise, and efficient solutions. This research underscores the transformative impact of AI in modernizing manufacturing processes, making it an indispensable tool for the future of the PCB industry.

Carryover slag weight prediction under converter tapping process by applying attention-shifting infrared thermal mapping technique

The prediction model of actual slag weight under converter tapping process was constructed by applying attention-shifting infrared thermal mapping technique. Firstly, the linkage between programmable logic controller signal of the converter mechanical structure and the infrared thermal mapping detector image was realized to ensure the real-time monitoring of the whole tapping process. Images should be preprocessed to ensure a good distinction between the flowing liquid and background at the tapping beginning, furthermore to separated slag from liquid steel at the following process. Moreover, the use of attention-shifting block belonging to the infrared thermal detector was effect to obtain more accurate image distinction between the liquid steel and slag. Hence the gray degree threshold value was adjusted to 220–225 in the early stage, and the value was needed to be lowered to 170–176 in the end. After using the model, the slag weight was less than &lt; 8.0kg/t when only slag-stopping awl was adopted in tapping process. The average weight of slag in converter was 7.3kg/t by using slag-stopping awl and sliding plate slag-blocking.

Prototype for Multi-UAV Monitoring–Control System Using WebRTC

Most unmanned aerial vehicle (UAV) ground control station (GCS) solutions today are either web-based or native applications, primarily designed to support a single UAV. In this paper, our research aims to provide an open, universal framework intended for rapid prototyping, addressing these objectives by developing a Web Real-Time Communication (WebRTC)-based multi-UAV monitoring and control system for applications such as automated power line inspection (APOLI). The APOLI project focuses on identifying damage and faults in power line insulators through real-time image processing, video streaming, and flight data monitoring. The implementation is divided into three main parts. First, we configure UAVs for hardware-accelerated streaming using the GStreamer framework on the NVIDIA Jetson Nano companion board. Second, we develop the server-side application to receive hardware-encoded video feeds from the UAVs by utilizing a WebRTC media server. Lastly, we develop a web application that facilitates communication between clients and the server, allowing users with different authorization levels to access video feeds and control the UAVs. The system supports three user types: pilot/admin, inspector, and customer. Our research aims to leverage the WebRTC media server framework to develop a web-based GCS solution capable of managing multiple UAVs with low latency. The proposed solution enables real-time video streaming and flight data collection from multiple UAVs to a server, which is displayed in a web application interface hosted on the GCS. This approach ensures efficient inspection for applications like APOLI while prioritizing UAV safety during critical scenarios. Another advantage of the solution is its integration compatibility with platforms such as cloud services and native applications, as well as the modularity of the plugin-based architecture offered by the Janus WebRTC server for future development.

Visualization of the degradation of long-acting microneedles and correlation of drug release in vivo based on FRET mechanism

This study introduces a live imaging technique for real-time, non-invasive monitoring of drug release from long-acting microneedles using FRET (Fluorescence Resonance Energy Transfer). Employing Cy5.5 and Cy7 as FRET pairs and levonorgestrel as the model drug, we fabricated microneedles with varying PLGA molecular weights, demonstrating distinct release profiles. The FRET-PLGA-10-MN demonstrated a rapid drug release profile, reaching nearly complete release within a two-day period, while FRET-PLGA-30-MN showed a sustained release over four days. Sensitized Emission FRET (SE-FRET) optimized the imaging process, providing a robust correlation between FRET signals and drug absorption. This method surpasses traditional pharmacokinetic studies by offering a more efficient and comprehensive analysis of microneedle release dynamics in vivo, paving the way for enhanced long-acting microneedle design and therapeutic outcomes. Statement of significance1. FRET technology was applied to microneedle drug delivery system for the first time, which realized real-time, quantitative and non-invasive monitoring of drug release process.2. The long-term microneedle technique was combined with sensitized emission method, and the FRET remaining ratio was innovatively used to investigate the FRET characteristics of microneedles, and the fluorescence ratio of FRET and donor double-channel was quantitatively calculated.3. The correlation between visual fluorescence images of FRET effect and semi-quantitative calculation results based on fluorescence intensity and drug release in vivo with drug-loaded microneedles was analyzed.

Dual-frequency countercurrent ultrasonic-assisted extraction of the cold brew coffee and in situ real-time monitoring of extraction process

Dual-frequency countercurrent ultrasonic-assisted extraction of the cold brew coffee and in situ real-time monitoring of extraction process

Decoding the silent language: A framework for advanced visual emotion recognition to enhance worker well-being on IIoT-enabled manufacturing

The Industrial Internet of Things (IIoT), pivotal in the Industry 4.0 revolution, has transformed manufacturing, particularly assembly lines, by enabling real-time process monitoring and data analysis to enhance productivity. Yet, its potential impact on worker well-being has been less explored. As the National Association of Manufacturers anticipates an opening of 2.1 million unfilled jobs by 2030, the necessity for frameworks that not only understand but also improve employee well-being becomes apparent. We introduce a novel, non-intrusive visual emotion recognition framework that incorporates facial emotion recognition (FER) and emotional body posture recognition (EBPR) to assess workers’ psychological states. This initiative aims to mitigate human errors and accidents linked to stress or high cognitive load, thereby enhancing the manufacturing environment. Strategies such as optimized break time planning, line balancing, procedure modifications, and improved training are proposed to ensure both employee well-being and stakeholder benefits. This framework indirectly supports the industry’s efforts to address the looming challenge of unfilled jobs by fostering a supportive work environment that enhances job satisfaction, potentially making positions more appealing and retaining valuable talent. By bridging the gap between technological advancements and human-centric considerations, our approach underscores the significance of integrating IIoT solutions with employee welfare initiatives to address both productivity and workforce sustainability challenges.

Harnessing interpretable and ensemble machine learning techniques for precision fabrication of aligned micro-fibers

Electrospinning is a robust technique for producing micro/nano-scale fibrous structures, influenced by intricate interplays of fluid dynamics, aerodynamics, and electromagnetic forces. Depending on the desired outcome, these fibers can adopt various morphologies, including solid, tubular, concentric, and gradient. Such morphologies are modulated by parameters such as collector configuration, flow rate, voltage, solution properties, and nozzle dimensions. However, the task of modeling and predicting these multifaceted morphologies remains complex. Aligned microfibers with 3D orientation hold promise in tissue engineering, regenerative medicine, and drug delivery, necessitating meticulous control over the fabrication parameters. In our research, we tapped into machine learning (ML) to address these challenges. Classification ML models were designed to predict fibrous patterns—aligned, random, or jet branching—based on determinants like voltage, flow rate, and collector configurations. Notably, the Random Forest (RF) and Support Vector Machine (SVM) models, especially with radial kernel-trick, displayed outstanding predictive capabilities on the test data. Furthermore, regression-based ML was harnessed to discern fiber alignment coherency and inter-fiber distances. Models such as Lasso and Ridge regression elucidated predictive coefficients for these characteristics, while ensemble models, like gradient-boosting (GB) decision trees (DT), showcased prowess in regression scenarios. Key findings spotlighted the significance of parameters like plate gap for alignment coherency and needle-to-collector distance for inter-fiber spacing. As we strive to gain granular control over micro/nano feature morphology in electrospinning, understanding predictor-response dynamics is imperative. Our investigation underscores the essential role of ML in enhancing both qualitative and quantitative precision in fabricating advanced fibrous structures. Moreover, fusing ML with real-time process monitoring offers groundbreaking potential, particularly in Bio-Fabrication, regenerative medicine, and tissue engineering, where high-precision manufacturing remains a top priority.

Engineering two-in-one DNA nanohybrids to evaluate anti-cancer effects through activatable RNA imaging

The development of DNA-based imaging techniques provides a promising way for theranostic applications. However, the dramatic chemical difference between DNA probes and therapeutics significantly hinders the construction of an ideal theranostic system for evaluation of therapeutic effects via in situ molecular imaging. In this work, we report a simple approach for the construction of a two-in-one DNA nanohybrid via one-step assembly of DNA probes and small molecular drugs, enabling evaluation of therapeutic effects via sensitive imaging of apoptosis-related mRNA. The nanohybrid was self-assembled from a rationally designed molecular beacon (MB) probe, doxorubicin (DOX, a chemotherapeutic drug) and Fe (II) ions through coordination interactions, which possesses anti-cancer effects from chemotherapeutics and the capability of efficient co-delivery of DNA probes without transfection agents. By tracking pro-apoptotic Bax mRNA expression with the MB, this system allows real-time monitoring of cell apoptotic process in response to drug treatment, enabling assessment of therapeutic effects of small molecular drugs. In addition, the approach is extended to the imaging of target microRNA in the drug treatment based on flexible DNA probe design, demonstrating the universality of this strategy. Moreover, the modular design of this DNA nanohybrid allows the introduction of photosensitizers into this system for efficient photodynamic therapy and simultaneous mRNA monitoring. This strategy expands the theranostic toolbox for the in situ evaluation of treatment response and screening anticancer drugs.

In situ Synchrotron X-ray Metrology Boosted by Automated Data Analysis for Real-time Monitoring of Cathode Calcination.

Synchrotron X-ray-based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on-site, on-the-fly analysis needed for real-time process monitoring. Here, a weighted lagged cross-correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X-ray diffraction metrology to monitor the calcination process of the archetypal nickel-based cathode, LiNiO2. The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO2, providing information for agile on-site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early-stage phase nucleation and growth. Expanding this data-powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.