Publications
Beyond linear calibration: A split-and-conquer AI architecture for MIP-based electrochemical sensing of C-reactive protein
C-reactive protein (CRP) is a critical biomarker for inflammation, necessitating rapid and precise quantification for the management of cardiovascular diseases and infections. This study introduces a Physics-Informed Regional Hybrid Machine Learning-assisted electrochemical sensor by integrating previously synthesized, high-specificity CRP-imprinted poly[HEMA-co-MMA-co-(S-rGO)] nanostructures into a Nafion-modified screen-printed carbon electrode (SPCE). Although these nanostructures provided superior molecular recognition, standard linear calibration failed to resolve the complex, non-linear kinetics inherent in the voltammetric signals. To address this, we implemented a novel Split-and-Conquer AI architecture, segmenting signals into anodic and cathodic domains based on peak potential. Specialized Random Forest regressors were trained using physics-informed features (e.g., logarithmic current) on a noise-augmented dataset, achieving a robust determination coefficient of R2 > 0.94. Crucially, a High-Resolution AI Signal Enhancement Protocol (Deep Oversampling) was deployed to suppress the stochastic instrumental noise floor observed within the sensor's physical linear range (0.24–15.44 μM). This software-defined approach drastically improved sensitivity, unlocking an unprecedented Limit of Detection (LOD) of 2.5 pM and Limit of Quantification (LOQ) of 8.4 pM. The system exhibited excellent selectivity against biological interferents like BSA and uric acid, retained 80.5% signal stability after eight regeneration cycles, and achieved high ML-assisted recovery rates of 95.9–119.3% across the full dynamic range (0.543–17.39 μM) in artificial blood matrices. This study confirms that combining established MIP nanotechnology with a segmented AI framework establishes a rapid, cost-effective, and laboratory-grade platform for next-generation point-of-care diagnostics.
Polymeric Nanomaterials Containing Amino Acids for Controlled Release of Triacetyluridine
Triacetyluridine (TAU) has higher oral bioavailability and represents a novel mechanism for delivering exogenous pyrimidines to the brain. It is commonly employed to mitigate the adverse effects of chemotherapy, attenuate toxicity, enhance the effectiveness of anticancer treatment, boost cognitive functions and memory, and provide therapeutic support for neurological and hereditary disorders. p(HEMA-MAC) was synthesized using non-surfactant emulsion polymerization. Then, they were modified for TAU specificity by using immobilized metal ion (Cu+’) affinity and characterized. Optimization of TAU adsorption conditions and controlled release studies of TAU were performed. At physiological Ph = 7.4, approximately 45.8% of 2 mg/mL TAU-loaded nanopolymers were shown to be controlled, efficient release within 90 min.
Artificial intelligence-assisted design of molecularly imprinted polymers for enriching C-reactive protein
The rational design of high-affinity biomimetic materials is often hampered by the high cost of experimental research. This study presents the development of artificial intelligence-assisted molecularly imprinted polymer (AI-MIP) nanostructures for the selective recognition of C-Reactive Protein (CRP), a clinically significant biomarker associated with inflammation and infection. The development of high-purity CRP isolation systems is clinically important for accurate diagnosis, reliable disease monitoring, and effective treatment planning.
To address these challenges, CRP-imprinted nanostructures were synthesized using a surfactant-free emulsion polymerization method, which ensured clean surface properties and high molecular specificity. Methyl methacrylate (MMA) and CRP were used in the formation of a pre-polymerization complex, and subsequently, 2-hydroxyethyl methacrylate (HEMA) and silanized reduced graphene oxide (S-rGO) were incorporated to obtain the final polymeric nanomaterial. After polymerization, CRP was successfully removed from the polymer matrix, yielding the CRP-imprinted poly[HEMA-co-MMA-co-(S-rGO)] structure. CRP binding studies confirmed the selective recognition of CRP by the imprinted nanostructure. CRP-imprinted nanostructure exhibited the highest binding affinity at a CRP concentration of 1 mg/mL, reaching a maximum binding capacity, Qmax, of 350 mg/g. Characterization studies, including FTIR, SEM, and zeta size and potential analysis (317 nm,–0.213 mV, respectively), validated the successful formation, structural integrity, and colloidal stability of the synthesized nanostructure. Unlike conventional recognition elements such as antibodies or aptamers, molecularly imprinted polymers (MIPs) offer a cost-effective and chemically stable alternative for CRP isolation and purification, demonstrating selectivity and specificity toward the target molecule. To reduce the experimental burden in terms of cost, time, and resource consumption, an experimentally constrained interpolation-based data augmentation strategy was implemented, enabling the construction of high-fidelity regression models from a minimal number of laboratory trials. Among the evaluated algorithms, CatBoost Regressor achieved superior performance, accurately predicting binding behavior under varying experimental conditions. Feature importance analysis highlighted reaction time as the most influential parameter affecting CRP binding performance. This study represents, to our knowledge, the first demonstration of a CRP-selective MIP designed through integrated AI and in-silico data augmentation techniques, offering a practical and scalable pathway for affinity material discovery. The approach not only accelerates material optimization but also significantly reduces the reliance on costly trial-and-error procedures.
Development of Magnetic Nanoparticle-Added Cryogel Membrane Materials for the Removal of Pharmaceutical Wastes
The increasing use of drugs, animal husbandry, health institutions, and the pharmaceutical industry is leading to an increase in pharmaceutical residues, which can cause neurological and cardiac diseases in living organisms. Removing these wastes is crucial for ensuring living health, environment, and water safety. New-generation materials, such as cryogel membranes, can be used for drug removal through analytical separation methods combining cryogel membranes and magnetic nanoparticles. In this study, magnetic mg-Fe3O4-APTES nanoparticle-doped p(HEMA)-phenylboronic acid (PBA) cryogel membranes for the removal of dopamine (DA), which was selected as the target molecule from pharmaceutical wastes, from industrial wastewater, were synthesized, characterized, modified on the basis of boronate affinity interaction, and DA adsorption and removal studies were carried out. The developed cryogel membranes were able to adsorb DA at a rate of 81.3% in just 15 min, with a very high adsorption capacity and specificity of 1969.1 mg/g, even after five adsorption–desorption cycles, and with high specificity and specificity in an artificial urine environment prepared to simulate hospital waste. It was concluded that it performed the correct removal. In the study, specific and reusable cryogel membranes that are easy to use, low-cost and provide high-capacity removal in a short time, which can also provide physical removal with their preparation and magnetic properties, were developed. The developed cryogel membranes have the potential to be used in the field for the removal of pharmaceutical wastes with these superior properties.
Harnessing Machine Learning to Revolutionize Electrochemical Detection of Vitamin E Acetate in E-Liquids
This study presents the development of a novel molecularly imprinted electrochemical sensor for the sensitive and selective detection of vitamin E acetate (VEA) in e-cigarette liquids, a critical step in addressing the rising public health concern of e-cigarette, or vaping, product use-associated lung injury. VEA-imprinted polymeric nanoparticles, intended to serve as the recognition element on the sensor surface, were synthesized using surfactant-free emulsion polymerization. The synthesized polymer was characterized using Fourier Transformed Infrared Spectroscopy, scanning electron microscope, and zeta potential analyses. The sensor, fabricated using VEA-imprinted poly(HMA-co-PA)/Nafion on screen-printed carbon electrodes, demonstrated a limit of quantification (LoQ) of 112.3 μg/mL (3.3× S/N) with a wide linear range extending to 3.0 mg/mL (10× S/N). While the sensor exhibited limitations in detecting VEA at concentrations below the LoQ, the integration of machine learning algorithms effectively mitigated these challenges. Machine learning models successfully classified the presence of VEA, even at subdetection limit concentrations, significantly enhancing the sensor’s analytical capabilities. Rigorous testing on real-world e-cigarette liquid samples yielded high recovery rates (96.83% ± 2.79–102.56% ± 3.84), validating the sensor’s accuracy and selectivity in complex matrices. This research not only establishes a promising platform for the rapid and sensitive detection of VEA in e-cigarette liquids but also underscores the transformative potential of integrating artificial intelligence with sensor technologies for addressing critical public health challenges.
Hybrid Intelligence-Driven Nanopolymeric Sensor for Precise Electrochemical Vitamin C Analysis,
Free from LoD: Application in Real Lemon Juice
This study presents the design, synthesis, and systematic evaluation of an electrochemical nanosensor platform tailored for the precise and selective quantification of vitamin C. The sensor architecture integrates His-functionalized poly(2-hydroxyethyl methacrylate-co-ethylene glycol dimethacrylate) (His-pHEG) polymeric nanoparticles onto Nafion-modified screen-printed carbon electrodes (SPCE), thereby providing a bioactive interface with enhanced analyte affinity and stability. The His-pHEG nanoparticles were synthesized via emulsion polymerization and covalently grafted with l-histidine, as confirmed by FTIR, SEM, and zeta potential analyses. This functionalization endowed the nanoparticles with enhanced affinity and high selectivity toward vitamin C molecules, while ensuring colloidal stability and uniform morphology. Sensor fabrication parameters, including Nafion film thickness and polymer concentration, were systematically optimized to maximize electrochemical performance. The resulting His-pHEG/Nafion-modified SPCE demonstrated superior analytical characteristics, achieving a low limit of detection and a broad linear dynamic range, as determined by cyclic voltammetry and differential pulse voltammetry measurements. To overcome the fundamental limitations of conventional calibration-based electrochemical methods, such as nonlinearity and variability, a two-stage hybrid machine learning framework, specifically tailored to the inherent nature of the sensor data, was developed and integrated into the sensing workflow. The two-stage model utilized CatBoost classification to distinguish analyte presence, followed by CatBoost regression to estimate vitamin C concentration, with hyperparameter optimization ensuring robustness and predictive accuracy. Real-sample validation using lemon juice confirmed the sensor’s high recovery rates and practical applicability, demonstrating reliable performance in complex matrices. This multidisciplinary approach bridges polymer chemistry, nanotechnology, electrochemical sensing, and artificial intelligence to deliver a portable, cost-effective, and highly sensitive vitamin C detection system. Future efforts will focus on translating this platform into mobile-based, real-time analytical devices, enabling on-site applications in food quality control, healthcare, and pharmaceutical industries.
Green Carbon Dots: Synthesis, Characterization,
Properties and Biomedical Applications
Carbon dots (CDs) are a new category of crystalline, quasi-spherical fluorescence, “zero-dimensional” carbon nanomaterials with a spatial size between 1 nm to 10 nm and have gained widespread attention in recent years. Green CDs are carbon dots synthesised from renewable biomass such as agro-waste, plants or medicinal plants and other organic biomaterials. Plant-mediated synthesis of CDs is a green chemistry approach that connects nanotechnology with the green synthesis of CDs. Notably, CDs made with green technology are economical and far superior to those manufactured with physicochemical methods due to their exclusive benefits, such as being affordable, having high stability, having a simple protocol, and being safer and eco-benign. Green CDs can be synthesized by using ultrasonic strategy, chemical oxidation, carbonization, solvothermal and hydrothermal processes, and microwave irradiation using various plant-based organic resources. CDs made by green technology have diverse applications in biomedical fields such as bioimaging, biosensing and nanomedicine, which are ascribed to their unique properties, including excellent luminescence effect, strong stability and good biocompatibility. This review mainly focuses on green CDs synthesis, characterization techniques, beneficial properties of plant resource-based green CDs and their biomedical applications. This review article also looks at the research gaps and future research directions for the continuous deepening of the exploration of green CDs.