This review addresses the recent developments in miniaturized microsystems or lab-on-a-chip devices for biosensing of different biomolecules: DNA, proteins, small molecules, and cells, especially at the single-molecule and single-cell level. In order to sense these biomolecules with sensitivity we have fabricated chip devices with respect to the biomolecule to be analyzed. The details of the fabrication are also dealt with in this review. We mainly developed microarray and microfluidic chip devices for DNA, protein, and cell analyses. In addition, we have introduced the porous anodic alumina layer chip with nanometer scale and gold nanoparticles for label-free sensing of DNA and protein interactions. We also describe the use of microarray and microfluidic chip devices for cell-based assays and single-cell analysis in drug discovery research.
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http://dx.doi.org/10.1007/10_2007_078 | DOI Listing |
Adv Exp Med Biol
January 2025
Department of Clinical Microbiology, Rigshospitalet, Copenhagen, Denmark.
Despite advances in healthcare, bacterial pathogens remain a severe global health threat, exacerbated by rising antibiotic resistance. Lower respiratory tract infections, with their high death toll, are of particular concern. Accurately replicating host-pathogen interactions in laboratory models is crucial for understanding these diseases and evaluating new therapies.
View Article and Find Full Text PDFSci Rep
January 2025
Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
This study advances microfluidic probe (MFP) technology through the development of a 3D-printed Microfluidic Mixing Probe (MMP), which integrates a built-in pre-mixer network of channels and features a lined array of paired injection and aspiration apertures. By combining the concepts of hydrodynamic flow confinements (HFCs) and "Christmas-tree" concentration gradient generation, the MMP can produce multiple concentration-varying flow dipoles, ranging from 0 to 100%, within an open microfluidic environment. This innovation overcomes previous limitations of MFPs, which only produced homogeneous bioreagents, by utilizing the pre-mixer to create distinct concentration of injected biochemicals.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, USA.
Effective heat dissipation remains a grand challenge for energy-dense devices and systems. As heterogeneous integration becomes increasingly inevitable in electronics, thermal resistance at interfaces has emerged as a critical bottleneck for thermal management. However, existing thermal interface solutions are constrained by either high thermal resistance or poor reliability.
View Article and Find Full Text PDFNano Lett
January 2025
School of Physics and Technology, and Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, Wuhan University, Wuhan 430072, China.
Hybrid nonlinear plasmonic waveguides, characterized by a small mode area and large nonlinear susceptibility, present an intriguing and practical platform for the minimization of nonlinear photonic devices. Nevertheless, the intrinsic Ohmic loss associated with surface plasmon polaritons (SPPs) and modal dispersion imposes constraints on the effective interaction length and, consequently, the ultimate efficiency of nonlinear processes. In this study, we demonstrate an efficient second harmonic generation (SHG) within a hybrid plasmonic waveguide by leveraging SPP-like modes at the fundamental wave and photonic-like modes at the SHG under phase matching conditions.
View Article and Find Full Text PDFSci Adv
January 2025
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano and IU.NET, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
Neurological disorders are a substantial global health burden, affecting millions of people worldwide. A key challenge in developing effective treatments and preventive measures is the realization of low-power wearable systems with early detection capabilities. Traditional strategies rely on machine learning algorithms, but their computational demands often exceed what miniaturized systems can provide.
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