Applications in the biomedical field require nanomaterials with improved properties. Single-walled carbon nanotubes (SWCNTs) filled with nickelocene are a new material for biomedical applications. The SWCNTs are filled with nickelocene using the gas-phase method. It is a very simple and environmentally friendly process that allows us to obtain new materials with high yield [1,2]. This method can be applied to materials for biomedical applications. The nickelocene-filled SWCNTs are obtained as a clean material that can be further characterized with spectroscopy. Indeed, it is known that spectroscopy requires high-quality samples. The spectroscopic samples should be free of impurities from the synthesis process. In this work, an investigation of nickelocene-filled SWCNTs with angle-resolved photoemission spectroscopy (ARPES) was performed [3]. Nickelocene-filled SWCNTs include metallicity-mixed SWCNTs, i.e., a mixture of metallic and semiconducting SWCNTs. It was shown that the encapsulated nickelocene leads to a donor effect on the SWCNTs. A variation in the Fermi level of the SWCNTs with different measurement angles was revealed. This was related to the modification of the electronic properties of the SWCNTs. The obtained data are important for biomedical applications of filled SWCNTs.

[1] Kharlamova M. V. et al. Nanoscale 2015, 7, 1383-1391.

[2] Kharlamova M. V. et al. Appl. Phys. A 2018, 124, 247.

[3] Kharlamova M. V. et al. Appl. Phys. A 2024, 130, 738.

Introduction

Rapid and accurate determination of trace amounts of C₂H₅OH in aqueous media of various origins is very important. The desire to improve the sensitivity and selectivity, as well as the durability of biosensors, has generated a huge amount of research. In our work, a peroxidase-type biomimetic sensor using Ag as a transducer was studied through potentiometric research.

Methods

The experiments were carried out in an electrochemical cell. The experimental setup for these studies included an electrode part, a cell, and a B7-21A universal voltmeter. The electrode part of the installation consisted of a reference electrode (Ag/AgCl/Cl^-) and a biomimetic sensor (TPhPFe³⁺/Al₂O₃/electrode) manufactured by us. The electrochemical installation was equipped with a magnetic stirrer to create an equilibrium solution. The background solution was double-distilled water.

Results

As a result of the research work, we found that the biomimetic sensor developed by TPhPFe³⁺/Al₂O₃/Ag allowed one to determine a given concentration in a few seconds. The sensitivity threshold was 10^-6 mass%.

Conclusions

The developed peroxidase-type biomimetic sensor based on smart material detected a low concentration of ethyl alcohol. The developed and synthesized peroxidase-type biomimetic sensor based on TPhPFe3+/Al2O3/Ag was highly sensitive, stable, and remained inactive for a long time. The sensitivity threshold for C2H5OH was 10-6 wt.%.

Bioreporters and biosensors can report not only external stimuli but also the internal conditions in the cell they are embedded in. Here, we used bioreporters to explore the effect of UV sources with different spectra on bacteria. UV irradiation is a common physical method for water disinfection and for the inactivation of pathogenic bacteria, viruses, and protozoa. Two types of UV lamps are used, i.e., Low-Pressure (LP) and Medium-Pressure (MP) lamps, where pressure relates to mercury vapor pressure.

MP lamps have polychromatic irradiation spectra, while LP lamps have an almost monochromatic spectrum, with 99% of the irradiation at ~254nm. In inactivation studies, MP lams have been shown to be more effective in preventing bacteria recovery, but the mechanisms underlying this phenomenon have not been fully understood.

Bioreporters offer a way to see the bacterial “point-of-view” of the irradiation. We studied the bacterial response to the two UV sources using E. coli strains containing different promoters upstream to the lux operon and exposing these bacteria to sub-lethal LP and MP irradiation. Following promotor activation, we found that MP irradiation results in the formation of Super Oxide (O₂•^-) inside the cells, probably due to the interaction of the MP lamp spectra's higher wavelength with specific amino acids. These radicals have also been found to play a major role in the disinfection process. These results could explain the disinfecting effects of UVA/B treatments that were previously described and could help in the design of better systems.

Cancer is regarded as a significant contributor to global mortality. If cancer is diagnosed at an early stage, it can be treated successfully. The existing cancer screening techniques are either invasive or prohibitively expensive. It is therefore imperative that a non-invasive and low-cost method of diagnosing cancer at an early stage is developed. The use of artificial nose devices that mimic the olfactory system of mammals represents a promising avenue for medical diagnosis. In recent years, we have developed arrays of fluorescent nanomaterials that are capable of simulating the receptors of the olfactory system. The nanomaterials have demonstrated considerable promise as a sensing platform, exhibiting high sensitivity. However, they exhibit low selectivity, demonstrating cross-reactivity towards a range of analogous chemical compounds. The use of a panel of cross-reactive fluorescent nanomaterials allows for the generation of a specific signal pattern for each chemical species, thereby enabling the analysis of complex samples. Artificial nose devices were fabricated using a paper-based platform, with the deposition of small amounts of metallic nanoclusters or carbon quantum dots. The devices are capable of detecting the odor of biological samples (VOCs), including blood, urine and exhaled breath, and can distinguish between individuals with and without cancer. In consequence, our devices are designed to operate on the basis of liquid or gas biopsies, which are classified among non-invasive or minimally invasive methods. The devices were employed to diagnose leukemia in children and hematologic malignancy in adults by measuring the VOCs emitted from the surface of blood. Additionally, the analysis of VOCs in the headspace of urine samples enabled the identification of colon and stomach cancers. Furthermore, the method is capable of detecting lung cancer through the analysis of VOCs in exhaled breath. The portability, low cost, and high speed of our devices make them ideal candidates for the rapid screening of cancer.

Alpha-synuclein (α-synuclein) is a key biomarker for neurodegenerative diseases, including Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). Its early and accurate detection in biological samples is crucial for timely diagnosis and disease management. This study presents a dual-platform biosensor that integrates electrochemical impedance spectroscopy (EIS) and surface plasmon resonance (SPR) for highly sensitive and specific α-synuclein quantification.

The electrochemical biosensor is based on nanostructured gold electrodes functionalized with monoclonal antibodies that selectively capture α-synuclein, inducing measurable impedance changes upon binding. SPR-based optical detection provides a label-free, real-time analysis of the same interaction, allowing for the cross-validation of results. The sensor was tested using spiked and clinical samples, demonstrating a detection limit of 12.5 pM (EIS) and 8.3 pM (SPR), with a linear range of 10 pM–100 nM. Both techniques exhibited high specificity, effectively distinguishing α-synuclein from common biofluid interferents.

This dual-detection strategy enhances the reliability, reproducibility, and accuracy of α-synuclein quantification, providing complementary insights into biomarker interactions. Furthermore, the biosensor's design enables potential miniaturization for portable point-of-care (POC) diagnostics, facilitating early intervention and improved disease monitoring. By integrating advanced biosensing technologies, this study addresses key challenges in biomarker detection, paving the way for more accessible and effective clinical diagnostics for neurodegenerative diseases.

A novel sensing material has been synthesized for the design of an electrochemical sensor for epinephrine detection. The sensing material is composed of poly(3,4-ethylenedioxythiophene) polymer (PEDOT), polydopamine (PDA), and gold nanoparticles (AuNPs). The use of low-cost chemicals and the development of water-based preparation methods without the need for organic solvents were intended to comply with a sustainable development goal in the sensor’s preparation. The biocompatibility and adhesion properties of PDA were explored in the sensor’s design. The inclusion of AuNPs featuring pronounced electrocatalytic properties was aimed at improving the analytical sensitivity. The sensing material has been synthesized onto glassy carbon electrodes by using cyclic voltammetry and sinusoidal tension and current methods. The morphological analysis of the sensing material was carried out by scanning electron microscopy. The sinusoidal currents method [1] achieved the best analytical performance of the PEDOT-PDA-AuNP sensor toward epinephrine determination. A linear response in the range of 0.4 - 100 μM and a detection limit value of 0.11 μM epinephrine were obtained. The sensing material shows improved antifouling properties and stability in the detection of epinephrine in synthetic samples. The detection of epinephrine in spiked synthetic and urine samples was achieved with good accuracy and minor interferences from uric acid. The sensor is designed for reuse of the electrodic substrate, while the modifier can be eliminated by local procedures. The analytical performance of the sensor is comparable with that of other sensors prepared with conventional methods.

References:

[1]. S.A. Leau et. al. Biosensors 14 (2024) 320.

Crop pathogens pose a significant threat to global agricultural production, resulting in substantial yield and economic losses. Conventional detection methods often exhibit limitations in accuracy, speed, and timely intervention. To address these challenges, this study presents a Pentagon-Shaped Terahertz (THz) Photonic Crystal Fiber (PCF) Biosensor integrated with a Decision-Cascaded 3D-Return-Dilated Secretary-Bird-Aligned Convolutional Transformer Network (DC3D-SBA-CTN). The proposed model employs a multi-stage feature extraction approach using Cascaded 3D-Dilated Convolutional Networks (CD-Net) and a Return-Aligned Decision Transformer (RADT) for accurate pathogen classification. Parameter optimization uses the Secretary-Bird Optimization Algorithm (SBOA), enhancing robustness and reducing false positives.

The biosensor's innovative pentagon-shaped design optimizes light–matter interactions, achieving heightened sensitivity and minimal signal loss. Simulation and experimental evaluations validate the biosensor's exceptional performance, with a detection accuracy of 99.9%, demonstrating resilience against morphological and environmental variations. Additionally, the system’s adaptability ensures its applicability across diverse agricultural settings, providing a reliable solution for real-time pathogen detection. The proposed solution enhances early intervention capabilities, contributing to reduced crop loss and increased agricultural productivity.

These findings establish the Pentagon-Shaped THz PCF Biosensor with DC3D-SBA-CTN as a transformative advancement in smart agricultural technology. The proposed approach contributes to precision farming and supports sustainable agricultural practices by enabling early detection and intervention.

Endocrine disorder affecting women in their reproductive age is called Polycystic Ovary Syndrome (PCOS), which leads to health consequences. Diagnostic methods fail to detect early signs of the disorder in the monitoring of PCOS biomarkers. Due to advancements in the healthcare domain, monitoring tools can now provide personalized treatment for patients. In continuous monitoring for PCOS, there is underutilization of wearable technologies, as existing devices fail in tracking biomarkers like hormones along with fluctuations in glucose. As current methods do not offer non-invasive, accessible techniques for detection at the early stages in management, the integration of wearable biosensors along with mobile technology presents aresearch gap. The aim is to develop an AI-driven wearable system that integrates both a mobile device and a biosensor for monitoring PCOS-related biomarkers including hormone levels and glucose variability. Using silicon photonics, the developed system helps in the detection of imbalances in hormone levels non-invasively by saliva analysis. Data are analyzed through the usage of YOLO algorithms, which are used for sending alerts based on the health of users. The Ava Bracelet, a wearable with a mobile-enabled biosensor system, integrates AI algorithms to monitor biomarkers related to PCOS through saliva. By employing nanomaterials and thin-film silicon photonic sensors, it provides precise and non-invasive detection of hormonal fluctuations. YOLOv8 is one of the significant models in AI that helps in the identification of anomalies; the engagement of users is also improved, and interventions are promptly facilitated. In contrast to existing works, the proposed system achieves an accuracy of 92%, a sensitivity of 89%, and a specificity of 94% while detecting abnormalities related to PCOS. The AI-driven system improves upon existing PCOS diagnostics through continuous, real-time monitoring and early detection of hormonal imbalances. Utilizing YOLOv8 enables rapid and accurate data processing, providing personalized insights and facilitating proactive health management. Biomarkers like luteinizing hormone (LH) and follicle-stimulating hormone (FSH) are targeted in detection by utilizing aptamer-based and antibody-based biorecognition elements.

Enzyme-based biosensors have emerged as a transformative technology, leveraging the specificity and catalytic efficiency of enzymes across various domains, including medical diagnostics, environmental monitoring, food safety, and industrial processes. These biosensors integrate biological recognition elements with advanced transduction mechanisms to provide highly sensitive, selective, and portable solutions for real-time analysis. This review explores the key components, detection mechanisms, and applications of and future trends in enzyme-based biosensors. Artificial enzymes, such as nanozymes, play a crucial role in enhancing enzyme-based biosensors by mimicking natural enzyme activity while offering improved stability, cost-effectiveness, and scalability. Their integration can significantly boost sensors' performance by increasing their catalytic efficiency and durability. The functionality of enzyme-based biosensors is built on three essential components: enzymes as biocatalysts, transducers, and immobilization techniques. Enzymes serve as the biological recognition elements, catalyzing specific reactions with target molecules to produce detectable signals. Transducers, including electrochemical, optical, thermal, and mass-sensitive types, convert these biochemical reactions into measurable outputs. Effective immobilization strategies, such as physical adsorption, covalent bonding, and entrapment, enhance the enzymes' stability and reusability, enabling their consistent performance. In medical diagnostics, they are widely used for glucose monitoring, cholesterol detection, and biomarker identification. Environmental monitoring benefits from these biosensors in detecting pollutants like pesticides, heavy metals, and nerve agents. The food industry employs them for quality control and contamination monitoring. Their advantages include high sensitivity, rapid response times, cost-effectiveness, and adaptability to field applications. Enzyme-based biosensors face challenges such as enzyme instability, interference from biological matrices, and limited operational lifespans. Addressing these issues involves innovations like the use of synthetic enzymes, advanced immobilization techniques, and the integration of nanomaterials such as graphene and carbon nanotubes. These advancements enhance enzymes' stability, improve their sensitivity, and reduce the detection limits, making this technology more robust and scalable.

Soil management technologies are key to tackling the UN development goals and ensuring the 70% increase in agricultural production needed by 2050. In particular, the determination of soil nutrients is crucial to optimising plant growth and maximising crop yields. However, the direct determination of ion availability is challenging due to environmental changes (e.g., soil moisture, temperature), and it requires costly and bulky equipment. While indirect methods, such as soil electric conductivity and hyperspectral imaging, offer advantages in terms of costs, their measurement accuracy is low, since they are influenced by a high number of parameters, including soil texture and composition. As such, there is a shortage of low-cost and accurate sensors to determine nutrient bioavailability in soil.

This work describes, for the first time, an impedimetric low-cost device for the simultaneous and direct determination of environmental parameters (i.e., temperature and humidity), as well as of the bioavailability of key ions in soil. The device could be incorporated into an Arduino-based setting, reducing manufacturing and operational costs and enabling flexible implementation across a wide range of settings. The impedimetric device was tested in vivo by implanting it into tomato plants and recording impedimetric signals over time. An AI algorithm was also trained to enable an accurate determination of ion concentrations. The final system could determine dynamic changes in Na⁺ and K⁺ with high accuracy (R²=0.98 in the case of sodium and R²=0.99 for potassium), and it could be used for the continuous monitoring of soil parameters.