Meticulous research for design of plasmonics sensors for cancer detection and food contaminants analysis via machine learning and artificial intelligence

Current research development on food contaminants, future risks, regulatory regime and detection technologies: A systematic literature review

Food contaminants pose serious threats to public health, with profound negative impacts on the economy, society, and environment. However, there is a lack of timely and comprehensive reviews on the latest developments in food contaminants and effective measures to prevent contamination, particularly through novel intelligent detection technologies and regulatory regimes. This study addresses this knowledge gap by presenting a timely review of the literature, focusing on current types of food contaminants, advances in detection technologies, emerging risks, and the latest developments in regulatory frameworks. The study reviewed 116 relevant articles published between 2019 and 2024 and conducted a thematic analysis. The food contaminants were classified into three categories: biological, chemical, and physical. The study identified six key drivers of current and future food safety risks: demographic change, economic factors, environmental conditions, geopolitical shifts, consumer priorities, and technological advancements. Findings reveal the uneven understanding of contaminants of emerging concern, future drivers of contaminants of emerging concern, and their impact on the food system, the environment, and human health. These findings highlight the need for future research on systematically identifying and validating the regional differences in food contamination prevention measures and assessing the extent to which these differences impact the effectiveness of prevention, mitigation, and control efforts. The findings also call for more international cooperation in food contamination research and the active involvement of technology partners to facilitate the application of cutting-edge technologies in food contamination detection and control.

Surface Plasmon Resonance-Based Biodetection Systems: Principles, Progress and Applications-A Comprehensive Review

Surface Plasmon Resonance (SPR)-based biodetection systems have emerged as powerful tools for real-time, label-free biomolecular interaction analysis, revolutionizing fields such as diagnostics, drug discovery, and environmental monitoring. This review highlights the foundational principles of SPR, focusing on the interplay of evanescent waves and surface plasmons that underpin its high sensitivity and specificity. Recent advancements in SPR technology, including enhancements in sensor chip materials, integration with nanostructures, and coupling with complementary detection techniques, are discussed to showcase their role in improving analytical performance. The paper also explores diverse applications of SPR biodetection systems, ranging from pathogen detection and cancer biomarker identification to food safety monitoring and environmental toxin analysis. By providing a comprehensive overview of technological progress and emerging trends, this review underscores the transformative potential of SPR-based biodetection systems in addressing critical scientific and societal challenges. Future directions and challenges, including miniaturization, cost reduction, and expanding multiplexing capabilities, are also presented to guide ongoing research and development in this rapidly evolving field.

Plasmonic nanoparticle sensors: current progress, challenges, and future prospects

Plasmonic nanoparticles (NPs) have played a significant role in the evolution of modern nanoscience and nanotechnology in terms of colloidal synthesis, general understanding of nanocrystal growth mechanisms, and their impact in a wide range of applications. They exhibit strong visible colors due to localized surface plasmon resonance (LSPR) that depends on their size, shape, composition, and the surrounding dielectric environment. Under resonant excitation, the LSPR of plasmonic NPs leads to a strong field enhancement near their surfaces and thus enhances various light-matter interactions. These unique optical properties of plasmonic NPs have been used to design chemical and biological sensors. Over the last few decades, colloidal plasmonic NPs have been greatly exploited in sensing applications through LSPR shifts (colorimetry), surface-enhanced Raman scattering, surface-enhanced fluorescence, and chiroptical activity. Although colloidal plasmonic NPs have emerged at the forefront of nanobiosensors, there are still several important challenges to be addressed for the realization of plasmonic NP-based sensor kits for routine use in daily life. In this comprehensive review, researchers of different disciplines (colloidal and analytical chemistry, biology, physics, and medicine) have joined together to summarize the past, present, and future of plasmonic NP-based sensors in terms of different sensing platforms, understanding of the sensing mechanisms, different chemical and biological analytes, and the expected future technologies. This review is expected to guide the researchers currently working in this field and inspire future generations of scientists to join this compelling research field and its branches.

AI-Assisted Detection of Biomarkers by Sensors and Biosensors for Early Diagnosis and Monitoring

The steady progress in consumer electronics, together with improvement in microflow techniques, nanotechnology, and data processing, has led to implementation of cost-effective, user-friendly portable devices, which play the role of not only gadgets but also diagnostic tools. Moreover, numerous smart devices monitor patients' health, and some of them are applied in point-of-care (PoC) tests as a reliable source of evaluation of a patient's condition. Current diagnostic practices are still based on laboratory tests, preceded by the collection of biological samples, which are then tested in clinical conditions by trained personnel with specialistic equipment. In practice, collecting passive/active physiological and behavioral data from patients in real time and feeding them to artificial intelligence (AI) models can significantly improve the decision process regarding diagnosis and treatment procedures via the omission of conventional sampling and diagnostic procedures while also excluding the role of pathologists. A combination of conventional and novel methods of digital and traditional biomarker detection with portable, autonomous, and miniaturized devices can revolutionize medical diagnostics in the coming years. This article focuses on a comparison of traditional clinical practices with modern diagnostic techniques based on AI and machine learning (ML). The presented technologies will bypass laboratories and start being commercialized, which should lead to improvement or substitution of current diagnostic tools. Their application in PoC settings or as a consumer technology accessible to every patient appears to be a real possibility. Research in this field is expected to intensify in the coming years. Technological advancements in sensors and biosensors are anticipated to enable the continuous real-time analysis of various omics fields, fostering early disease detection and intervention strategies. The integration of AI with digital health platforms would enable predictive analysis and personalized healthcare, emphasizing the importance of interdisciplinary collaboration in related scientific fields.

Navigating the development of silver nanoparticles based food analysis through the power of artificial intelligence

In the ongoing pursuit of enhancing food safety and quality through advanced technologies, silver nanoparticles (AgNPs) stand out for their antimicrobial properties. Despite being overshadowed by other nanoparticles in food sensing applications, AgNPs possess inherent qualities that make them effective tools for rapid and selective contaminant detection in food matrices. This review aims to reinvigorate the interest in AgNPs in the food industry, emphasizing their sensing mechanism and the transformative potential of integrating them with artificial intelligence (AI) for enhanced food safety monitoring. It discusses key AI tools and principles in the food industry, demonstrating their positive impact on food analytical chemistry. The interplay between AI and biosensors offers many advantages and adaptability to dynamic analytical challenges, significantly improving food safety monitoring and potentially redefining the landscape of food safety and quality assurance.

Observation of Plasmonics Talbot effect in graphene nanostructures

We report on the theoretical models of the plasmoincs Talbot effect in graphene nanostructure. The Talbot effect for the plasmonics applications in the IR range is theoretically studied and the respective Talbot effect for the novel advanced plasmonics structures are numerically investigated for the first time. It is shown that the metamaterial structures with periodic grating configuration represents a complex three-dimensional lattice of beamlet-like graphene plasmonics devices. The calculated results agree well with the experimental ones. The results obtained can be used to create and optimize the structures considering diffraction limit for a wide range of application areas. Effective focusing of plasmonic waves with exact focal spots and a subwavelength full width at half maximum can be obtained by using periodic graphene grating.