2,4-Dinitrophenol (2,4-DNP) is a highly toxic nitroaromatic compound that has attracted considerable attention because of its presence in explosive materials used in mining activities. Continuous exposure to 2,4-DNP poses serious health risks, including hyperthermia, metabolic disruption, oxidative stress, and potentially fatal toxicity. Even at low concentrations, 2,4-DNP can interfere with mitochondrial oxidative phosphorylation and may lead to severe physiological disturbances. Such exposure risks are particularly concerning for mine workers who operate in harsh environments and may encounter contaminated water. Therefore, the development of highly sensitive and selective electrochemical sensors for the detection of 2,4-DNP in mining environments is crucial in safeguarding the health and safety of mine workers. Although various metal-based electrochemical sensors have been reported for the detection of 2,4-DNP, their practical applicability remains limited due to poor stability and the insufficient exposure of catalytic active sites, which often result in reduced sensing performance. To address these limitations, we report the synthesis of a silica-supported heteroatom-doped (N, C, and S) bimetallic CoNi nanocomposite (NCS-CoNi@SiO2). The SiO₂ surface was smartly decorated with Co and Ni nanoparticles to maximize the exposure of catalytic active sites. Meanwhile, simultaneous N, C, and S doping not only enhances the structural stability to the CoNi wrapping but also promotes greater active-site exposure and introduces abundant defect sites, thereby improving overall sensing capability. The designed sensor exhibits high sensitivity, an ultralow detection limit of 26.7 nM, and excellent selectivity even in the presence of potential interfering species. These results highlight a promising and scalable strategy for the reliable detection of 2,4-DNP in mining wastewater.

Creatinine plays a crucial role in monitoring renal performance, highlighting the need for accurate and efficient detection approaches. This work presents the fabrication of a cobalt-praseodymium bimetallic metal-organic framework (Co-Pr-BDC MOF) using terephthalic acid as a linker through a solvothermal route. Comprehensive characterization by FTIR, PXRD, SEM, EDX, TGA, and UV-Vis spectroscopy confirmed the formation of a crystalline, porous framework with favourable stability. The interaction of the synthesized MOF with creatinine was investigated to assess its sensing potential. The binding of creatinine to the MOF resulted in a noticeable blue shift in the UV-Vis absorption spectrum along with an increase in absorbance, attributed to coordination interactions at the metal centres and possible hydrogen bonding. The system demonstrated a linear response to varying creatinine concentrations, achieving detection limits of 0.12 mM. These results highlight the potential of the Co-Pr-BDC MOF as a practical and efficient material for creatinine detection, suitable for applications in biosensing and clinical analysis.

Transition metals are increasingly replacing noble-metal-based OER electrocatalysts due to their low cost and availability. However, they often face challenges of low electrocatalytic activity and stability. To address these limitations, bimetallic or composite materials have been extensively developed, leveraging synergistic effects to enhance catalytic efficiency. Unfortunately, optimizing these systems remains challenging, as the individual contributions of each component to the overall electrocatalytic performance are not yet fully understood, and limited attention has been given to this issue. To address this issue, herein we employed machine learning (ML) algorithms to optimize and identify the most influential component governing OER electrocatalytic efficacy. We synthesized a bimetallic OER catalyst by coating electrospun nanofibers of polyaniline (PA) and cellulose acetate (CA) on nickel foam (NF), followed by drop-casting of CuO-NiO (CNO) onto the nanofiber surface. ML was applied to optimize and construct the best-fit combination of the designed bimetallic OER catalyst. Results reveal that ML-optimized CNO/CA-PA@NF shows high electrocatalytic activity, showing a low overpotential of 326 mV at 10 mA cm-2, Tafel slope of 52 mV dec-1, and an onset potential of 1.48 V vs RHE. Additionally, it shows high stability, which could be ascribed to cohesive interfacial interactions between CNO and CA-PA nanofibers. To the best of our knowledge, this is the first report to highlight the transformative role of ML optimization in advancing bimetallic transition-metal-based electrocatalysts, thus paving the way for durable and efficient OER systems for sustainable energy applications.

Premature drug release and poor controllability present significant challenges in the practical application of cancer therapy, often resulting in reduced chemotherapy effectiveness and severe side effects. A key limitation of current anticancer nanocarriers lies in reconciling multiple functionalities with biodegradability and favorable biocompatibility. To meet these challenges, herein, we have synthesized novel trimetallic (Cu, Fe, Zn) squarate based MOF (STM) and L-ascorbate based MOF (LTM) by a simple solvothermal approach under controlled reaction conditions. Furthermore, polydopamine (PDA) wrapped MOF composite hollow nanoparticles were fabricated by utilizing silica nanoparticles as a template to enhance drug encapsulation capacity and controlled drug release. Both of the trimetallic MOF showed irregular morphology with layered structure and after decorated with PDA, SPTM and LPTM exhibited spherical shape with size of around 100 nm confirmed with scanning electron microscopy. Both newly synthesized trimetallic MOF, SPTM@DOX and LPTM@DOX showed high DOX loading efficiency 98.45, and 99.09

Although a wide range of bimetallic metal-organic frameworks (MOFs) have been reported as electrocatalysts for the oxygen evolution reaction (OER), there is still a need for precise tuning of the metal precursors and composite ratios to minimize the overpotential and design highly efficient electrocatalysts. To achieve this, we applied machine learning (ML) algorithms to optimize the metal precursor and composite ratios, identifying the key factors that govern the OER performance. We first synthesized a bimetallic FeCo squarate-based MOF (FeCo-Sq MOF) using a solvothermal method and then optimized the metal precursor ratios using ML algorithms to achieve a low overpotential. To further enhance the OER efficacy, the ML-optimized FeCo-Sq MOF was coated with S-doped graphitic carbon nitride (SCN) and wrapped with polydopamine (PDA). The PDA wrapping not only increased the number of binding/adsorption sites for -OH but also enhanced the stability, charge/electron transfer kinetics, and effective anchoring of SCN on the MOF surface. To obtain optimal OER catalysts, the SCN loading was further fine-tuned through ML. The ML-optimized PDA-SCN@FeCo-Sq MOF exhibited high electrocatalytic performance, achieving a low overpotential of 310 mV and a Tafel slope of 56 mV/dec at a current density of 10 mA cm-2 in 1 M KOH. This study presents a promising ML-assisted strategy for designing high-performance PDA-SCN@FeCo-Sq MOF electrocatalysts for efficient water splitting.

This chapter provides a comprehensive overview of electrochemical sensors, explaining their fundamental principles, classification, design, and emerging trends as well as future perspectives. Electrochemical sensors are recognized for their ability to convert chemical information into electrical signals, offering advantages such as high sensitivity, selectivity, and real-time monitoring, making them integral across diverse applications, including environmental monitoring, medical diagnostics, and industrial processes. Based on the type of measurement, electrochemical sensors are classified into potentiometric, conductometric, impedimetric, voltammetric, and coulometric sensors. The evolving landscape of electrochemical sensing encompasses advancements in electrode modification, electrolyte optimization, and ion-selective membranes. Integration of nanomaterials, microfluidic devices, aptamers, and machine learning fosters enhanced sensitivity, specificity, and portability, paving the way for advanced point-of-care diagnostics and personalized healthcare. This chapter concludes with a contemplation of future perspectives, envisioning the progress driven by emerging technologies.

Electrochemical sensors based on nanomaterials have the potential to revolutionize industries such as environmental monitoring and healthcare. These sensors need exact control over synthesis, surface modification, and integration with signal amplification mechanisms because of their enormous surface area, enhanced conductivity, and catalytic qualities. Improved reaction kinetics, less interference from intricate sample matrices, and exact control over sample delivery are all made possible using microfluidic devices in sensor designs. Sensing applications have made a significant contribution to the use of metallic elements like gold (Au), platinum (Pt), palladium (Pd), silver (Ag), copper (Cu), cobalt (Co), and rare earth metals. The most frequently utilized nanomaterials, graphene, carbon nanotubes (CNTs), and gold nanoparticles (Au NPs), are known for their strong electrocatalytic features, high surface energy, and simple integration abilities. To comprehend the structure–property correlations of nanomaterials and maximize sensor performance, precise characterization methods such as electron microscopy and spectroscopy are essential. Upcoming developments will concentrate on improving miniaturization, specificity, and sensitivity for in situ and point-of-care (POC) testing. All things considered, electrochemical sensors based on nanomaterials have a lot of promise for addressing societal issues.

The compound 8-hydroxy-2 '-deoxyguanosine (8-OHdG) is a key byproduct of oxidative DNA damage and is widely recognized as an important biomarker for assessing DNA oxidation levels. This study presents a label-free, low-cost, smart sensor that can improve evaluation, tracking, and survival rates by allowing for an early assessment of cancer. Herein, we fabricate sulfur-doped graphitic carbon nitride (S-gC3N4) embedded in polycaprolactone (PCL) for highly efficient monitoring of 8-OHdG. The S-gC3N4 offers functional groups such as sulfur and nitrogen that facilitate strong binding interactions with 8-OHdG. Comprehensive techniques are utilized to investigate the S-gC3N4/PCL nanocomposite. Interestingly, the S-gC3N4/PCL nanocomposite demonstrates strong electrochemical responses to the oxidation of 8-OHdG, with a low detection limit across a wide dynamic concentration range (1 nM-50 mu M). Additionally, it exhibits good durability, selectivity, reusability, and repeatability. The developed sensor has the potential to quantify 8-OHdG levels in individuals and can be used to evaluate oxidative DNA damage and risk factor for cancer. Furthermore, the S-gC3N4/PCL-based sensor is successfully tested to determine 8-OHdG levels in human serum samples.

Although various heteroatom-doped bimetallic composites have been explored for the oxygen evolution reaction (OER), they often suffer from aggregation and low electrical conductivity, which hinder their electrocatalytic efficacy. Thus, to overcome these challenges, herein, we have employed machine learning (ML) optimization to precisely control the growth of bimetallic Co and Ni nanoparticles on silica (SiO2) nanospheres (CoNi@SiO2), thus mitigating the aggregation effect. Additionally, the amount of thiourea, which serves as the source for S and N doping, was optimized using ML. The results reveal that NS-doped CoNi@SiO2 exhibits enhanced electrocatalytic performance by providing more exposed active sites. The results reveal that the designed ML optimized NS-doped CoNi@SiO2-based electrode has shown promising OER activity by exhibiting low overpotential (220 mV) and onset potential (1.28 V vs. RHE) compared to CoNi@SiO2 (300 mV, 1.29 V), SiO2 (340 mV, 1.34 V vs. RHE) and NF (341.7 mV, 1.51 V vs. RHE). The enhanced performance of NS-doped CoNi@SiO2 can be attributed to the synergistic effects of NS doping and the bimetallic system, which leads to an increase in the exposition of active sites and surface area. This work highlights the potential of ML optimization in fine-tuning electrocatalyst composition to enhance electrocatalytic performance.

Despite significant advancements in noble metal-free bimetallic and trimetallic composite-based electrochemical sensors for efficient p-nitrophenol (p-NP) monitoring, limited attention has been given to identifying which metallic component plays the most critical role in governing electrocatalytic efficacy, leaving this area largely unexplored. Additionally, the development of efficient sensing systems remains challenging by the persistent challenge of achieving high sensitivity and selectivity. To address these challenges, herein we have developed a highly efficient electrochemical sensor for p-NP by synthesizing a NiCoMn-based trimetallic metal-organic framework (NCM) and integrating it into highly conductive polycaprolactone (PCL) electrospun nanofibers. To achieve optimal catalytic efficacy, the synthesized composite was first optimized using machine learning (ML) to determine the best-fit composition. The optimized composite was then incorporated into PCL electrospun nanofibers, promoting a smooth and uniform ion flow across the electrode surface. To enhance selectivity for pNP, the NCM integrated PCL (NCM-PCL) was further coated with polydopamine (PD), which introduced functional groups such as catechol, amine, and quinone, facilitating strong binding and precise interaction with p-NP. The results demonstrated that the engineered NCM-PCL wrapped with PD (NCM-PCL@PD) based electrochemical sensor achieved an impressive detection limit of 2.38 nM (S/N = 3) across a concentration range of 5 to 1000 nM. Additionally, the sensor displayed exceptional stability and selectivity towards p-NP in electrochemical measurements, thus underscoring its potential for real-time analysis and practical applications.

Injudicious boron (B) supply to correct its deficiency may lead to toxicity and a decline in crop growth and yield. This study was conducted for two consecutive seasons during 2022 and 2023 with the objective to optimize B supply for better grain yield and quality of wheat. Experimental plan comprised three B levels by soil application (control, 2 and 4 kg ha-1), two foliar spray levels (0.2 and 0.4% B solution), and combination of soil plus foliar application making total nine treatments. B was applied as nanoparticles (B-NPs) and boric acid (H3BO3). B nutrition at 2 kg ha-1 B-NPs while 4 kg ha-1 H3BO3 caused highest improvement in growth, yield, physiological, biochemical, and grain quality characteristics. Grain yield was maximally improved by 24.40 and 22.67% with 2 kg ha-1 B-NPs while, 20.18 and 19.0% with 4 kg ha-1 H3BO3 during 2022 and 2023, respectively, compared to the control. B application at higher level, particularly in soil plus foliar pattern caused toxicity as evidenced by an increase of 114.2 and 123.6% in H2O2 while, 89.47 and 93.22% in malondialdehyde (MDA) concentration with 4 kg ha-1+0.4% solution of B-NPs during 2022 and 2023, respectively, compared to control. Soil B was fractionated into six functionally active fractions. Of the total soil B, plant available B ranged from 0.59 to 2.48% with B-NPs while, 0.55-1.89% with H3BO3. In conclusion, B application at 2 kg ha-1 B-NPs while, 4 kg ha-1 H3BO3 could be recommended for optimum wheat productivity under alkaline calcareous conditions.

Despite a wide range of noble metal-free electrocatalysts having been developed for efficient oxygen evolution reactions (OERs) to date, there is a need to optimize (i.e., dopant concentration, material deposited, drying time, etc.) these electrocatalysts to get the best overpotential. Thus, herein, we have studied the impact of Zr doping concentration (1, 2, 3, 4, and 5%) in CeO2 along with experimental condition optimization as a function of overpotential through machine learning (ML) to design a highly efficient OER electrocatalyst. Our results demonstrated that the ML-optimized 3% Zr-doped CeO2 electrode showed maximum electrocatalytic activity with lower onset potential (1.39 V vs RHE), overpotential (380 mV at 10 mA cm-2), and Tafel slope (85.7 mV dec-1) compared to CeO2 doped with various concentrations of Zr. Additionally, ML-optimized 3% Zr-doped CeO2 has shown better stability with 84% current density retention after 48 h, thus suggesting the reliability of our designed system.

This work presents a chlorophyll-cysteine-capped cadmium selenide conjugate (Chl-Cys-CdSe) probe for the sensitive and selective detection of creatinine, a critical biomarker for renal dysfunction analysis. The study aims to address the need for precise and selective detection tools by enhancing the emission properties of cysteinecapped CdSe quantum dots through modification with chlorophyll. Characterization of material was conducted using fourier transform infrared spectroscopy, energy-dispersive X-ray spectroscopy, transmission electron microscopy, and X-ray diffraction. The results revealed that the Chl-Cys-CdSe conjugate exhibited a hexagonal morphology with an average particle size of 7-8 nm. The sensing mechanism relies on the fluorescence quenching of the Chl-Cys-CdSe QDs, which is most pronounced at pH 8, corresponding to optimal interactions with creatinine. The color changes in the paper strips were captured under UV light and quantified using ImageJ software to measure RGB intensity. The detection limits were determined through paper-based analysis, fluorescent, and UV-Visible spectrometric techniques. These results highlight the probe sensitivity and selectivity for creatinine detection. The Chl-Cys-CdSe conjugate-based probe represents a promising advancement in the field of renal health monitoring, offering a versatile and robust detection system that integrates fluorescent and colorimetric methods with advanced imaging techniques. This approach holds significant potential for clinical diagnostics and real-time health monitoring.

A precise monitoring of Pseudomonas aeruginosa in cow milk sample by using polydopamine functionalized Co-EDTA complex-based electrochemical aptasensor.

Oxidative stress, a major key factor to neurological disorders such as Parkinson's, Alzheimer's, and Huntington's disease. Ascorbic acid (AA), a vital brain antioxidant, protects neurons by scavenging reactive oxygen species, and its fluctuations can damage neuronal function. Therefore, precise monitoring of AA in humans is critical for early diagnosis and disease management. However, despite significant advancements in noble metal-free nanocomposite based electrochemical sensors, precise detection remains challenging due to low physiological concentrations and interference from coexisting biomolecules, which limit sensor sensitivity, selectivity, and real-time applicability. To address these limitations herein, we synthesized cobalt-doped nickel oxide (Co@NiO), wrapped with thiourea, and dopamine (SCN- wrapped Co@NiO) for selective and sensitive detection of AA. The SCN groups provide abundant binding sites through hydrogen-bonding interactions with AA, thereby enhancing selectivity, while the Co@NiO nanostructure catalytically facilitates AA oxidation, significantly improving sensitivity and enabling efficient sensing efficacy against interfering species. The findings demonstrated that the fabricated material exhibits excellent sensitivity (0.1837 μA/nM/cm2), a wide linear range (5nM-20μM), and low detection limit (12.72 nM). Furthermore, the designed electrode has shown excellent selectivity towards AA even in presence of various interfering species, highlighting its potential in real-time analysis for practical applications.

The development of innovative materials that possess remineralization and antibacterial efficacy to reduce white spot lesions (WSLs) are becoming growing clinical demands. To overcome these WSLs, bioactive glass (BG) is widely acknowledged due to its bioactivity. To introduce subsidiary biological functions, BG was doped with therapeutic ions i.e., Ag and Cu, that significantly improve antibacterial properties. In this study, Ag/Cu was co-doped into BG (A/C@BG) by sol-gel method. Ion release studies done by ICP-OES verified sustained release of Ag/Cu, placed in SBF while in-vitro bioactivity supported formation of hydroxyapatite layer. Antibacterial studies of A/C@BG demonstrated significant increase in inhibition zones, measuring 14 and 10 mm against S. mutans and P. gingivalis. Moreover, mechanical studies demonstrate SBS of adhesive loaded with A/C@BG compared with control, does not decrease significantly. The results suggested A/C@BG possesses improved efficacy as bioactive filler that may facilitate antibacterial characteristics to prevent WSLs while maintaining optimal SBS.