Polarization-dependent plasmonic nano-tweezer as a platform for on-chip trapping and manipulation of virus-like particles

Dielectrophoresis: Measurement technologies and auxiliary sensing applications

Dielectrophoresis (DEP), which arises from the interaction between dielectric particles and an aqueous solution in a nonuniform electric field, contributes to the manipulation of nano and microparticles in many fields, including colloid physics, analytical chemistry, molecular biology, clinical medicine, and pharmaceutics. The measurement of the DEP force could provide a more complete solution for verifying current classical DEP theories. This review reports various imaging, fluidic, optical, and mechanical approaches for measuring the DEP forces at different amplitudes and frequencies. The integration of DEP technology into sensors enables fast response, high sensitivity, precise discrimination, and label-free detection of proteins, bacteria, colloidal particles, and cells. Therefore, this review provides an in-depth overview of DEP-based fabrication and measurements. Depending on the measurement requirements, DEP manipulation can be classified into assistance and integration approaches to improve sensor performance. To this end, an overview is dedicated to developing the concept of trapping-on-sensing, improving its structure and performance, and realizing fully DEP-assisted lab-on-a-chip systems.

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

Cancer is one of the leading causes of death worldwide, making early detection and accurate diagnosis critical for effective treatment and improved patient outcomes. In recent years, machine learning (ML) has emerged as a powerful tool for cancer detection, enabling the development of innovative algorithms that can analyze vast amounts of data and provide accurate predictions. This review paper aims to provide a comprehensive overview of the various ML algorithms and techniques employed for cancer detection, highlighting recent advancements, challenges, and future directions in this field. The main challenge is finding a safe, auditable and reliable analysis method for fundamental scientific publication. Food contaminant analysis is a process of testing food products to identify and quantify the presence of harmful substances or contaminants. These substances can include bacteria, viruses, toxins, pesticides, heavy metals, allergens, and other chemical residues. Machine learning (ML) and artificial intelligence (A.I) proposed as a promising method that possesses excellent potential to extract information with high validity that may be overlooked with conventional analysis techniques and for its capability in a wide range of investigations. A.I technology used in meta-optics can develop optical devices and systems to a higher level in future. Furthermore (M.L.) and (A.I.) play key roles as a health Approach for nano materials NMs safety assessment in environment and human health research. Beside, benefits of ML in design of plasmonic sensors for different applications with improved resolution and detection are convinced.

Photonic Quasi-Crystal Fiber-Based Plasmonic Biosensor: a Platform for Detection of Coronavirus

Since the coronavirus pandemic began, research groups worldwide developed diagnostic tests. One of the promising platforms for testing is an optical and plasmonic biosensor. Localized surface plasmon resonances owing to their highly concentrated field intensity provide highly sensitive devices. A beneficial approach to excite localized surface plasmon modes for field-based applications is using photonic crystal fibers while photonic quasi-crystals demonstrate a higher order of symmetry, the more isotropic Brillouin zone, and the easier achievement of photonic bandgap as compared with conventional photonic crystals. In this work, by exploiting a photonic quasi-crystal fiber, we are designing a surface plasmon resonance biosensor for the on-chip and real-time detection of coronaviruses. In our miniaturized design, a thin gold layer is employed on the outer layer of an air hole of a photonic quasi-crystal fiber with a 12-fold symmetry where the leakage of the fiber core mode can excite the surface plasmon resonance mode on the gold. According to three-dimensional finite-difference time-domain simulations, the proposed biosensor shows the sensitivity of 1172 nm/RIU in the detection of coronaviruses within the saliva. Moreover, the smallest detection limit obtained in the simulation is about 12 nm. These promising results altogether indicate that this reconfigurable and lab-on-a-chip platform can potentially be used in the detection of all kinds of coronaviruses.