Biosensor Enhancement Using Grooved Micromixers: Part II, Experimental Studies

Surface Acoustic Wave-Driven Enhancement of Enzyme-Linked Immunosorbent Assays: ELISAW

Enzyme-linked immunosorbent assays (ELISAs) are widely used in biology and clinical diagnosis. Relying on antigen-antibody interaction through diffusion, the standard ELISA protocol can be time-consuming, preventing its use in rapid diagnostics. We present a time-saving and more sensitive ELISA without changing the standard setup and protocol, using surface acoustic waves (SAWs) to enhance performance. Each step of the assay, from the initial antibody binding onto the walls of the well plate to the target analyte molecules' binding for detection─except, notably, for the blocking step─is improved principally via acoustic streaming-driven advection. Using SAWs, the time required for one step of an example ELISA is reduced from 60 to 15 min to achieve the same binding amount. By extending the duration of SAW exposure to 20 min, the sensitivity can be significantly improved over the 60 min, 35 °C ELISA without SAWs. It is also possible to confer beneficial improvements to bead-based ELISA by combining it with SAWs to further reduce the time required for binding to 2 min. By significantly increasing the speed of ELISA, its utility may be improved for a wide range of point-of-care diagnostics applications.

Ten Years Progress of Electrical Detection of Heavy Metal Ions (HMIs) Using Various Field-Effect Transistor (FET) Nanosensors: A Review

Heavy metal pollution remains a major concern for the public today, in line with the growing population and global industrialization. Heavy metal ion (HMI) is a threat to human and environmental safety, even at low concentrations, thus rapid and continuous HMI monitoring is essential. Among the sensors available for HMI detection, the field-effect transistor (FET) sensor demonstrates promising potential for fast and real-time detection. The aim of this review is to provide a condensed overview of the contribution of certain semiconductor substrates in the development of chemical and biosensor FETs for HMI detection in the past decade. A brief introduction of the FET sensor along with its construction and configuration is presented in the first part of this review. Subsequently, the FET sensor deployment issue and FET intrinsic limitation screening effect are also discussed, and the solutions to overcome these shortcomings are summarized. Later, we summarize the strategies for HMIs' electrical detection, mechanisms, and sensing performance on nanomaterial semiconductor FET transducers, including silicon, carbon nanotubes, graphene, AlGaN/GaN, transition metal dichalcogenides (TMD), black phosphorus, organic and inorganic semiconductor. Finally, concerns and suggestions regarding detection in the real samples using FET sensors are highlighted in the conclusion.

Porous Silicon-Based Aptasensors: Toward Cancer Protein Biomarker Detection

The anterior gradient homologue-2 (AGR2) protein is an attractive biomarker for various types of cancer. In pancreatic cancer, it is secreted to the pancreatic juice by premalignant lesions, which would be an ideal stage for diagnosis. Thus, designing assays for the sensitive detection of AGR2 would be highly valuable for the potential early diagnosis of pancreatic and other types of cancer. Herein, we present a biosensor for label-free AGR2 detection and investigate approaches for enhancing the aptasensor sensitivity by accelerating the target mass transfer rate and reducing the system noise. The biosensor is based on a nanostructured porous silicon thin film that is decorated with anti-AGR2 aptamers, where real-time monitoring of the reflectance changes enables the detection and quantification of AGR2, as well as the study of the diffusion and target-aptamer binding kinetics. The aptasensor is highly selective for AGR2 and can detect the protein in simulated pancreatic juice, where its concentration is outnumbered by orders of magnitude by numerous proteins. The aptasensor's analytical performance is characterized with a linear detection range of 0.05-2 mg mL^-1, an apparent dissociation constant of 21 ± 1 μM, and a limit of detection of 9.2 μg mL^-1 (0.2 μM), which is attributed to mass transfer limitations. To improve the latter, we applied different strategies to increase the diffusion flux to and within the nanostructure, such as the application of isotachophoresis for the preconcentration of AGR2 on the aptasensor, mixing, or integration with microchannels. By combining these approaches with a new signal processing technique that employs Morlet wavelet filtering and phase analysis, we achieve a limit of detection of 15 nM without compromising the biosensor's selectivity and specificity.

Characterization of Mixing Performance Induced by Double Curved Passive Mixing Structures in Microfluidic Channels

Functionalized sensor surfaces combined with microfluidic channels are becoming increasingly important in realizing efficient biosensing devices applicable to small sample volumes. Relaxing the limitations imposed by laminar flow of the microfluidic channels by passive mixing structures to enhance analyte mass transfer to the sensing area will further improve the performance of these devices. In this paper, we characterize the flow performance in a group of microfluidic flow channels with novel double curved passive mixing structures (DCMS) fabricated in the ceiling. The experimental strategy includes confocal imaging to monitor the stationary flow patterns downstream from the inlet where a fluorophore is included in one of the inlets in a Y-channel microfluidic device. Analyses of the fluorescence pattern projected both along the channel and transverse to the flow direction monitored details in the developing homogenization. The mixing index (MI) as a function of the channel length was found to be well accounted for by a double-exponential equilibration process, where the different parameters of the DCMS were found to affect the extent and length of the initial mixing component. The range of MI for a 1 cm channel length for the DCMS was 0.75–0.98, which is a range of MI comparable to micromixers with herringbone structures. Overall, this indicates that the DCMS is a high performing passive micromixer, but the sensitivity to geometric parameter values calls for the selection of certain values for the most efficient mixing.

3D-printed microfluidics integrated with optical nanostructured porous aptasensors for protein detection

Microfluidic integration of biosensors enables improved biosensing performance and sophisticated lab-on-a-chip platform design for numerous applications. While soft lithography and polydimethylsiloxane (PDMS)-based microfluidics are still considered the gold standard, 3D-printing has emerged as a promising fabrication alternative for microfluidic systems. Herein, a 3D-printed polyacrylate-based microfluidic platform is integrated for the first time with a label-free porous silicon (PSi)-based optical aptasensor via a facile bonding method. The latter utilizes a UV-curable adhesive as an intermediate layer, while preserving the delicate nanostructure of the porous regions within the microchannels. As a proof-of-concept, a generic model aptasensor for label-free detection of his-tagged proteins is constructed, characterized, and compared to non-microfluidic and PDMS-based microfluidic setups. Detection of the target protein is carried out by real-time monitoring reflectivity changes of the PSi, induced by the target binding to the immobilized aptamers within the porous nanostructure. The microfluidic integrated aptasensor has been successfully used for detection of a model target protein, in the range 0.25 to 18 μM, with a good selectivity and an improved limit of detection, when compared to a non-microfluidic biosensing platform (0.04 μM vs. 2.7 μM, respectively). Furthermore, a superior performance of the 3D-printed microfluidic aptasensor is obtained, compared to a conventional PDMS-based microfluidic platform with similar dimensions.

Technical Model of Micro Electrical Discharge Machining (EDM) Milling Suitable for Bottom Grooved Micromixer Design Optimization

In this paper, development of a technical model of micro Electrical Discharge Machining in milling configuration (EDM milling) is presented. The input to the model is a parametrically presented feature geometry and the output is a feature machining time. To model key factors influencing feature machining time, an experimental campaign by machining various microgrooves into corrosive resistant steel was executed. The following parameters were investigated: electrode dressing time, material removal rate, electrode wear, electrode wear control time and machining strategy. The technology data and knowledge base were constructed using data obtained experimentally. The model is applicable for groove-like features, commonly applied in bottom grooved micromixers (BGMs), with widths from 40 to 120 µm and depths up to 100 µm. The optimization of a BGM geometry is presented as a case study of the model usage. The mixing performances of various micromixer designs, compliant with micro EDM milling technology, were evaluated using computational fluid dynamics modelling. The results show that slanted groove micromixer is a favourable design to be implemented when micro EDM milling technology is applied. The presented technical model provides an efficient design optimization tool and, thus, aims to be used by a microfluidic design engineer.

A New Approach for the Diagnosis of Myelodysplastic Syndrome Subtypes Based on Protein Interaction Analysis

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematological malignancies with a high risk of transformation to acute myeloid leukemia (AML). MDS are associated with posttranslational modifications of proteins and variations in the protein expression levels. In this work, we present a novel interactomic diagnostic method based on both protein array and surface plasmon resonance biosensor technology, which enables monitoring of protein-protein interactions in a label-free manner. In contrast to conventional methods based on the detection of individual biomarkers, our presented method relies on measuring interactions between arrays of selected proteins and patient plasma. We apply this method to plasma samples obtained from MDS and AML patients, as well as healthy donors, and demonstrate that even a small protein array comprising six selected proteins allows the method to discriminate among different MDS subtypes and healthy donors.

Multiparametric detection of bacterial contamination based on the photonic crystal surface mode detection

Conventional techniques for food and water quality control and environmental monitoring in general have a number of drawbacks. Below we propose a label-free highly accurate analytical technique for multiplex detection of biomarkers based on the analysis of propagation of Bloch waves on the surface of a photonic crystal. The technique can be used to measure molecular and cell affinity interactions in real time by recording critical and excitation angles of the surface wave on the surface of a photonic crystal. Based on the analysis of photonic crystal surface modes, we elaborated a protocol for the detection of the exotoxin A of Pseudomonas aeruginosa and the heat-labile toxin LT of Escherichia coli. The protocol exploits detection of affinity interactions between antigens pumped through a microfluidic cell and detector antibodies conjugated to the chemically activated silica chip. The proposed technique is highly sensitive, cheap and less time-consuming in comparison with surface plasmon resonance.

Optoelectrofluidic enhanced immunoreaction based on optically-induced dynamic AC electroosmosis

We report a novel optoelectrofluidic immunoreaction system based on electroosmotic flow for enhancing antibody-analyte binding efficiency on a surface-based sensing system. Two conventional indium tin oxide glass slides are assembled to provide a reaction chamber for a tiny volume of sample droplet (∼5 μL), in which the top layer is employed as an antibody-immobilized substrate and the bottom layer acts as a photoconductive layer of an optoelectrofluidic device. Under the application of an AC voltage, an illuminated light pattern on the photoconductive layer causes strong counter-rotating vortices to transport analytes from the bulk solution to the vicinity of the assay spot on the glass substrate. This configuration overcomes the slow immunoreaction problem of a diffusion-based sensing system, resulting in the enhancement of binding efficiency via an optoelectrofluidic method. Furthermore, we investigate the effect of optically-induced dynamic AC electroosmotic flow on optoelectrofluidic enhancement for surface-based immunoreaction with a mathematical simulation study and real experiments using immunoglobulin G (IgG) and anti-IgG. As a result, dynamic light patterns provided better immunoreaction efficiency than static light patterns due to effective mass transport of the target analyte, resulting in an achievement of 2.18-fold enhancement under a growing circular light pattern compared to the passive mode.