Label-Free Kinetic Biosensing on a 3D-Printed Microfluidic Chip Integrated with an Optical Transmittance Setup
M.Sekhwama1,2
K.T.Mpofu1✉Emailkmpofu@csir.co.za
M.P.Mcoyi1,2
S.Sudesh2
P.Mthunzi-Kufa1,2,3
1Biophotonics, Council for Scientific and Industrial ResearchNational Laser Centre0001Pretoria, GautengSouth Africa
2Division of Biomedical Engineering, Department of Human BiologyUniversity of Cape Town7701Cape TownWestern CapeSouth Africa
3School of Interdisciplinary Research and Graduate Studies (UNESCO), College of Graduate StudiesUniversity of South AfricaPreller StreetMuckleneuk Ridge, PretoriaSouth Africa
M. Sekhwama1,2, K.T. Mpofu1, M.P. Mcoyi1,2, S. Sudesh 2, and P. Mthunzi-Kufa 1,2,3
1 Biophotonics, Council for Scientific and Industrial Research, National Laser Centre, Pretoria, 0001, Gauteng, South Africa.
2 Division of Biomedical Engineering, Department of Human Biology, University of Cape Town, Cape Town, 7701, Western Cape, South Africa.
3 School of Interdisciplinary Research and Graduate Studies (UNESCO), College of Graduate Studies, University of South Africa, Preller Street, Muckleneuk Ridge, Pretoria, South Africa.
*Corresponding author: kmpofu@csir.co.za
Abstract
The development of reliable and affordable point-of-care (PoC) diagnostic devices is increasingly critical in the context of recurring global epidemics and pandemics. In this study, we present an optics-based transmittance biosensing experiment for HIV diagnostics, utilizing a biotinylated probe that binds specifically to a neutravidin-functionalized sensing surface. The sensing substrate consists of a thin gold-coated glass slide integrated into a custom-designed, 3D-printed microfluidic chip, which enables improved sample handling, automation, and reduced risk of cross-contamination. To ensure stable transmittance measurements, flow rate optimization was performed across a range of 9.5 to 12 mL/min, with 10.5 mL/min yielding the most stable signal and subsequently used for all experiments. The transmittance kinetics quantifying changes in optical transmission upon analyte binding were extracted from the real-time transmitogram data. These parameters were evaluated across varying probe concentrations (diluted in PBS) to establish a quantitative relationship suitable for diagnostic purposes. Furthermore, Monte Carlo simulations were employed to statistically analyze the variability and robustness of the kinetic parameters, contributing to the development of a novel diagnostic standard based on transmittance kinetics. The originality of this work lies in its integration of real-time optical sensing with microfluidics and kinetic modeling, advancing transmittance-based diagnostics as a viable platform for PoC HIV detection.
Keywords:
Point-of-care diagnostics
Microfluidics
Additive manufacturing
Optical biosensing
Transmittance kinetics
HIV detection
Real-time biosensing
Monte Carlo simulation
Gold-coated sensor
Surface functionalization
A
1. Introduction
Microfluidics is an interdisciplinary field that investigates the behavior and control of fluids at the microscale and nanoscale, combining principles from biology, physics, chemistry, and engineering [1–2]. The development of microfluidic devices has revolutionized a range of applications by enabling precise manipulation of small liquid volumes, typically in the microliter or nanoliter range [3–4]. These devices, often fabricated as microfluidic chips, are now integral to various industrial and biomedical processes, where they support functions such as sample preparation, mixing, and targeted delivery [5–6]. The integration of microfluidics with biosensing platforms has led to significant advancements in sensitivity, speed, and automation of diagnostic technologies [7–8]. In particular, the ability of microfluidic systems to reduce sample volumes, minimize cross-contamination, and enable multiplexed detection makes them attractive for point-of-care (PoC) applications. A well-known example is the lateral flow assay (LFA), which allows rapid detection of analytes in complex biological matrices with minimal user input [1].
The fabrication of microfluidic chips has evolved with the adoption of techniques such as photolithography, soft lithography, injection molding, and increasingly, additive manufacturing or 3D printing [9–13]. In this study, we employed a 3D-printed microfluidic chip fabricated using stereolithography (SLA), a technique that offers cost-effective, rapid prototyping of microscale components. This approach is particularly beneficial for research environments in resource-constrained settings, such as those in developing countries, where the affordability and reproducibility of diagnostic tools are critical [14]. In addition to being cost-efficient, 3D printing provides flexibility in design and the ability to rapidly iterate on device prototypes [15]. The chip used in this study was fabricated using a clear epoxy resin selected for its optical clarity, biocompatibility, and chemical resistance properties essential for biosensing applications [16]. This work builds on our previous efforts [13] by introducing a novel kinetic transmittance-based biosensing methodology. Specifically, we integrate the 3D-printed microfluidic chip into a custom-built optical setup that leverages a transmittance detection scheme for the real-time analysis of molecular interactions.
Transmittance-based biosensing involves measuring the intensity of light that passes through a biological sample, where changes in transmitted light can be correlated to the presence and behavior of target analytes [17–18]. Because certain wavelengths are absorbed by specific molecular structures, the spectral profile of transmitted light provides insight into sample composition. This method is well-suited for biosensing due to its simplicity, non-invasiveness, and compatibility with real-time monitoring [19–21]. Such systems typically consist of a broadband light source, a transparent sample holder, and a detector (e.g., a CCD spectrometer). When light passes through a sample, variations in intensity at specific wavelengths can be used to infer molecular binding events or concentration changes [22–24]. Advances in photodetector sensitivity, miniaturized light sources, and nanomaterials have further improved the analytical performance of these sensors [25].
A
To date, limited work has been done to investigate the kinetics of analyte interactions in transmittance-based biosensing setups. Our study addresses this gap by introducing a kinetic analysis framework that quantifies the rate of signal change as a function of analyte concentration. We further strengthen the statistical reliability of these findings using Monte Carlo simulations, which enable robust modeling of signal variability and kinetic parameter extraction. The experimental platform (Fig. 1) employs a gold-coated glass substrate functionalized with neutravidin, allowing biotinylated HIV-1 oligonucleotide probes to bind selectively at the reaction site. A Gilson Minipuls 3® peristaltic pump was used to regulate flow rates during sample delivery through the microfluidic channel. The resulting transmission intensity profiles (transmitograms) were analyzed to extract kinetic parameters and assess their dependence on probe concentration.Fig. 1
Custom-built optical biosensor illustration integrated with a microfluidic chip.
While optical biosensors offer compelling advantages, such as label-free detection, high specificity, and real-time analysis, they can also be sensitive to external perturbations such as ambient light fluctuations, vibrations, and temperature drift [26–28]. The integration of microfluidics mitigates many of these issues by stabilizing the sample environment and enhancing repeatability. Furthermore, microfluidic platforms support automation and multiplexing, which are essential features for portable and scalable diagnostic systems [29], [31–32]. Emerging technologies such as machine learning [33, 44], quantum-enhanced biosensing [34–42] aptamers [43], and microcontroller technologies [45] are increasingly being integrated into biosensor design to improve detection limits, interpret complex datasets, and push the boundaries of sensitivity and precision. Our study contributes to this growing body of knowledge by proposing a practical, cost-effective transmittance-based platform with kinetic modeling capabilities, offering potential utility as a diagnostic standard.
2. Materials and Methods
2.1 Design and additive manufacturing of microfluidic chips
The microfluidic chip used in this study was designed using Autodesk Fusion 360, a widely adopted computer-aided design (CAD) platform. The chip features a lateral flow configuration, incorporating two channels, one inlet and one outlet, with inner diameters of 2.8 mm and outer diameters of 3 mm. The primary microchannel, intended for sample transport, has a width of 0.8 mm and a length of 3 mm. Central to the chip is a circular reaction chamber with a diameter of 8 mm and a height of 1.5 mm, serving as the designated interaction zone for optical interrogation. The full footprint of the chip is 26 mm × 26 mm.
Figure 2 illustrates the finalized design:
Figure 2a presents a two-dimensional schematic with key dimensions, and
Figure 2b depicts the corresponding three-dimensional rendering extruded from the sketch.
A dedicated bottom compartment was incorporated into the design to securely mount a gold-coated glass substrate (20 mm × 20 mm) using a thin layer of silicone adhesive glue, ensuring a tight optical interface for transmittance-based measurements. The finalized design was exported as an STL file and uploaded into PreForm, the slicing software tailored for Formlabs 3B + SLA 3D printers. The chip was printed using Formlabs Clear V4 resin, chosen for its optical transparency, mechanical strength, and biocompatibility, essential properties for biosensing applications involving optical detection. The print was executed at the highest resolution setting of 25 µm (0.025 mm), with the complete print taking approximately 1 hour and 20 minutes.
Post-processing involved multiple steps:
Channel clearance was performed using a pressure pump to ensure unobstructed flow paths.
The printed chip was immersed in isopropyl alcohol using the Form Wash station for 20 minutes to remove uncured resin.
Final curing was conducted in the Form Cure system at 65°C for 10 minutes, enhancing mechanical stability and resin cross-linking.
The choice of Clear V4 resin allows for near-optical clarity, making it ideal for applications requiring light transmission or visual access to internal features. This material is commonly used in the fabrication of LED enclosures, fluidic devices, optical components, transparent windows, and molds, among others.
Fig. 2
Lateral flow microfluidic chip designed with Autodesk Fusion 360. a) 2-dimensional sketch of the microfluidic chip with measurements in mm, b) 3-dimensional microfluidic chip designed.
2.2 Mounting of the gold-coated slide to the microfluidic chip
A microscope glass slide (76 mm × 26 mm, Lasec SA (Pty) Ltd, South Africa) was cut to a square dimension of 20 mm × 20 mm using a precision diamond cutter to match the recess in the microfluidic chip design. This slide served as the substrate for plasmonic functionalization. Gold coating was achieved via physical vapor deposition (PVD) using a system from Korvus Technology Ltd. (UK) housed at the CSIR National Laser Centre (Biophotonics Laboratory). The substrate was first coated with a 10 nm adhesion layer of titanium (Ti), followed by a 50 nm layer of gold (Au) to ensure a uniform, optically active surface suitable for surface chemistry and biosensing. Following deposition, the coated slide was carefully aligned with the underside of the 3D-printed microfluidic chip, ensuring full coverage of the reaction chamber. The slide was secured in place using a thin, uniform layer of silicone adhesive, applied to avoid leakage or optical distortion at the interface. The assembled chip was then left to cure overnight at ambient conditions to ensure strong bonding and structural stability before use in subsequent functionalization and optical experiments.
Fig. 3
illustrates the gold-coated glass substrate post-deposition, prior to integration into the microfluidic chip.
Figure 3
Gold-coated slide mounted on microfluidic chip, A) Illustration of treatment of the gold-coated slide with 3MPTS (3-Mercaptopropyltrimethoxysilane), B) activation of the surface with GMBS (N-(4-Maleimidobutyryloxy) sulfosuccinimide, C) the attachment of Neutravidin on the activated surface coated with gold.
2.3 Treatment and activation of the surface for the attachment of NeutrAvidin
Following gold deposition and chip assembly, the sensor surface was functionally prepared to enable stable immobilization of NeutrAvidin for subsequent biosensing. The gold-coated glass slide, pre-mounted to the microfluidic chip, was first cleaned and activated through a multi-step chemical treatment. The surface was sonicated for 15 minutes in absolute ethanol (RADCHEM, South Africa) followed by distilled water (dH₂O) to remove organic and particulate contaminants. The slide was then dried using a stream of high-purity nitrogen (N₂) gas.
To promote hydroxylation and enhance surface hydrophilicity, the slide underwent oxygen plasma treatment, which improved surface reactivity and prepared it for silanization. Silanization was performed by incubating the chip for 2 hours in a 2% (v/v) solution of 3-Mercaptopropyltrimethoxysilane (3-MPTS) (Sigma-Aldrich) in absolute ethanol. This step introduced thiol (-SH) functional groups onto the surface, providing a molecular bridge for crosslinker attachment. Following incubation, the chip was thoroughly rinsed and sonicated in phosphate-buffered saline (PBS) to remove unbound silane molecules, then dried again with nitrogen gas.
Next, the thiol-functionalized surface was activated using N-γ-maleimidobutyryl-oxysuccinimide ester (GMBS) (Thermo Fisher Scientific, South Africa), a heterobifunctional crosslinker that reacts with thiol groups to introduce maleimide-activated esters. The chip was incubated with GMBS for 45 minutes, enabling robust coupling chemistry for protein immobilization. Post-activation, the chip was rinsed and sonicated in PBS for 5 minutes, ensuring the removal of excess reagent. After a final nitrogen drying step, the surface was immediately incubated with NeutrAvidin (Thermo Fisher) in PBS and allowed to bind overnight at 4°C in a humidified chamber. This sequence of silanization, crosslinking, and protein immobilization ensured a stable, high-density layer of NeutrAvidin on the gold surface, facilitating high-affinity binding of biotinylated probes and preserving biosensor specificity and reproducibility.
2.4 Integration of microfluidic chips into optical biosensor setup
The functionalized microfluidic chip was integrated into a custom-built transmittance-based optical biosensing platform for real-time diagnostics. The chip was firmly mounted onto the optical stage using precision alignment clamps to ensure stability during measurement and to maintain optical alignment between the light source and the detection axis. The reaction chamber was carefully positioned to coincide with the optical path of the broadband light beam, allowing maximum transmission through the sensing region. Flexible pressure-resistant tubing was connected to the chip’s inlet and outlet ports and securely sealed using parafilm to prevent leakage or pressure loss during flow.
The inlet tubing was connected to a sample reservoir containing the biotinylated oligonucleotide probe solution, while the outlet tubing directed the waste fluid into a collection vial. A Gilson Minipuls 3® peristaltic pump (Lasec SA (Pty) Ltd) was used to drive the fluid at controlled flow rates, ensuring consistent delivery of the sample through the microchannel. The optical system employed a broadband white light source spanning wavelengths from 400 to 700 nm, which was directed through the reaction site. Transmitted light was captured using a CCD spectrometer, and real-time intensity data were recorded using SpectraSuite software (Ocean Insight). This configuration enabled the acquisition of transmittance profiles (transmitograms) in response to biomolecular interactions occurring at the sensor surface.
Figure 4 summarizes the complete integration workflow:
(1) depicts the fabricated 3D-printed lateral flow microfluidic chip;
(2) and (3) show the gold-coated slide functionalized with silane chemistry and immobilized NeutrAvidin, mounted to the chip;
(4) illustrates the chip integrated into the optical setup with the data acquisition system connected for real-time analysis.
This modular setup allows for repeatable biosensing experiments with precise control over sample delivery and optical interrogation, offering a reliable platform for the study of transmittance-based kinetics in biosensing.
.
Fig. 4
Stepwise process of collecting transmitted light data. 1) 3D-designed LFMC using Autodesk fusion 360.2) a printed and post-processed microfluidic chip mounted with Au-coated slide functionalized with suitable saline and NeutrAvidin.3) microfluidic chip ready for use. 4) optical setup integrated with a microfluidic chip for real-time light transmission study.
3. Results
3.1 Transmission Intensity Profiling and Flow Rate Optimization
Transmission spectroscopy was employed to investigate the real-time interaction dynamics between biotinylated HIV-1 oligonucleotide probes and surface-immobilized NeutrAvidin. The goal was to assess the stability and reproducibility of transmitted light intensity during analyte flow, and to identify optimal flow conditions for subsequent kinetic analyses.
A series of preliminary experiments were conducted to evaluate the effect of varying flow rates, from 9.0 to 13.0 mL/min, on the stability of the transmitted light intensity. These measurements were carried out using a broadband white light source (400–700 nm) and recorded via a CCD spectrometer. The light was transmitted through the functionalized sensing region of the gold-coated microfluidic chip.
The data revealed that a flow rate of 10.5 mL/min provided the most stable intensity-versus-time profile with minimal noise and drift, as depicted in Fig. 5. Therefore, this flow rate was selected as the operating condition for all subsequent biosensing experiments. A sample volume of 100 µL was used consistently across all trials to ensure uniform residence time in the detection chamber.
Fig. 5
Transmitted intensity vs. time profiles for different flow rates of biotinylated oligonucleotide probe. A flow rate of 10.5 mL/min was identified as optimal based on signal stability.
3.2 Effect of Concentration on Transmittance Intensity
Using the optimized flow rate, we investigated the effect of probe concentration on the transmitted light intensity by preparing various mixtures of the biotinylated oligonucleotide probe in phosphate-buffered saline (PBS). Three experimental conditions were evaluated:
100 µL PBS (blank)
20 µL PBS / 80 µL probe
40 µL PBS / 60 µL probe
Each mixture was introduced into the microfluidic chip at 10.5 mL/min, and the resulting transmittance intensity profiles were recorded in real-time. As shown in Fig. 6, distinct intensity signatures were observed for each concentration.
Fig. 6
Transmission intensity vs. time of different concentrations of 100
of PBS, 20
PBS/ 80
probe, and 40
PBS/ 60
probe.
3.3 Analysis of Results and Kinetic Parameter Estimation
To evaluate the system’s ability to resolve differences in molecular interaction dynamics, we analyzed the transmitted intensity profiles for three sample conditions:
100 µL PBS (blank) (Eq. 3 below)
40 µL PBS / 60 µL probe (Eq. 1 below)
20 µL PBS / 80 µL probe (Eq. 2 below)
Each profile was fitted to an exponential decay model to extract transmittance-based kinetic parameters, defined as the rate at which transmitted light stabilizes in response to analyte interaction with the surface-bound NeutrAvidin. The following fitted equations were obtained for each condition:...
1
2
3
Where I(t) is the transmitted intensity in milliwatts (mW), and ttt is time in seconds. As expected, the blank PBS condition (Eq. 3) exhibited the fastest decay rate (largest kinetic parameter), likely due to the absence of analyte binding, while samples containing the oligonucleotide probe exhibited slower decay as a result of specific molecular interactions with the immobilized NeutrAvidin.
Figure 7 illustrates the intensity vs. time profiles for the three concentrations. Notably, the 20 µL PBS / 80 µL probe sample produced the highest transmitted intensity (~ 4150 mW) and the lowest decay rate, reflecting sustained binding events and higher optical throughput. Conversely, the PBS-only condition stabilized around 1600 mW, indicating minimal interaction at the sensor surface.
Figure 7 shows:
a.
(a) Transmission profile for 20 µL PBS / 80 µL probe (blue curve)
b.
(b) 40 µL PBS / 60 µL probe (red curve)
c.
(c) 100 µL PBS control (orange curve)
Fig. 7
a) Results (yellow curve) of the transmitted intensity versus time of volume 20
PBS and 80
oligonucleotide HIV probe, b) Results (blue curve) of the transmitted intensity versus time (s) of volume 100
( 40
PBS and 60
biotinylated oligonucleotide probe), c) Results (Brown curve) of the transmitted intensity vs. time profile of 100
volume of PBS introduced to the immobilized surface with NeutrAvidin.
The fitted kinetic parameter values, coefficient of determination (R²), and Monte Carlo simulation errors are summarized in Table 1. These values confirm excellent model fit, with R² values > 0.99 for all conditions.
Concentrations | value | Kinetic parameter | Simulated absolute error |
|---|---|---|---|
100 | 0.993717 | -0.373352 | - |
40 PBS/ 60 probe | 0.999968 | -0.017296 | |
20 PBS / 80 probe | 0.999924 | -0.0111996 |
To validate these observations, Monte Carlo simulations (n = 100) were performed using Mathematica. Each transmitogram was fitted individually to extract a distribution of kinetic parameter values. The 20 µL PBS / 80 µL probe sample exhibited the most consistent kinetic behavior, with the lowest simulated absolute error, indicating high reproducibility and stability of the interaction signal. These findings are visualized in Fig. 8, which compares kinetic parameter distributions across all concentrations.
The analysis confirms a concentration-dependent optical response, where increased probe content correlates with reduced kinetic decay rates and higher transmitted intensity. This relationship affirms the sensor's ability to differentiate binding strengths via real-time transmittance changes. Moreover, the strong correlation between intensity and probe concentration highlights the potential of transmittance-based kinetic parameters as a diagnostic metric. These results demonstrate that 3D-printed microfluidic chips, when integrated into an optical biosensing platform, offer reliable performance for real-time, label-free molecular detection. Future work will focus on improving environmental shielding of the setup to mitigate external disturbances such as ambient light fluctuations, vibration, and temperature drift, factors known to introduce signal variability in optical biosensors.
Fig. 8
Kinetic parameters vs. concentration analysis of 100
PBS (red point), 20
PBS/ 80
probe (blue point) and 40
PBS/ 60
probe concentrations (purple point) after Monte Carlo simulation.
4. Conclusion
In this study, we successfully designed and fabricated a functional microfluidic chip using Autodesk Fusion 360 and Formlabs SLA 3D printing technology, demonstrating the viability of rapid, low-cost prototyping for biosensing applications. This work highlights the power of additive manufacturing in producing customized microfluidic platforms tailored for optical diagnostics an approach that is especially relevant for resource-limited and point-of-care (PoC) settings where conventional microfabrication may be inaccessible.
We integrated the printed chip into a custom-built transmittance-based optical biosensing system, incorporating a gold-coated sensing surface functionalized with NeutrAvidin to capture biotinylated oligonucleotide probes. By systematically varying the concentration of probe solutions and analyzing real-time transmitted light profiles, we introduced and validated a novel kinetic parameter to characterize signal evolution in response to molecular interactions. Unlike traditional endpoint measurements, our approach captures the dynamic behavior of transmitted intensity over time, offering deeper insight into binding kinetics and interaction strength. We observed a clear inverse relationship between analyte concentration and the kinetic decay rate, with higher probe concentrations leading to slower decay constants and higher stabilized intensities. This finding suggests that the proposed transmittance-based kinetic parameter could serve as a sensitive and quantitative metric for analyte detection offering a new dimension of analysis for optical biosensing.
Furthermore, the experimental configuration demonstrated excellent signal stability at an optimized flow rate of 10.5 mL/min, underscoring the importance of flow dynamics in preserving measurement accuracy and reproducibility. Our results were supported by, which confirmed the robustness of the fitted parameters and highlighted the system’s potential for repeatable diagnostics. Beyond its analytical contributions, this work also addresses key challenges in the field namely, the need for portable, user-configurable biosensing systems. The combination of 3D-printed microfluidics with optical detection offers a scalable path toward compact, modular lab-on-a-chip devices capable of real-time, label-free analysis. Such systems are poised to transform diagnostics in decentralized environments, including clinics, field sites, and mobile health units.
In conclusion, this study presents both a technological innovation and a methodological advancement. The use of kinetic transmittance modeling in a 3D-printed microfluidic system provides a new diagnostic framework with potential applications in infectious disease screening, environmental monitoring, and personalized healthcare. Future work will focus on expanding the analyte scope, minimizing environmental interference, and integrating advanced data processing tools including machine learning for enhanced signal interpretation and automation.
Availability of data and materials.
There is no additional information associated with this paper.
A
Author Contribution
All authors contributed equally to this work. Masindi Sekhwama, Kelvin Mpofu and Phumlani Mcoyi contributed to the writing and reviewing of the manuscript. Sivarasu Sudesh and Patience Mthunzi-Kufa contributed to the review of the manuscript.
Conflict of Interest.
The authors declare that they have no conflict of interest.
A
Funding
Declaration. The authors acknowledge the Council for Scientific and Industrial Research (CSIR) and the Department of Science and Innovation (DSI) for funding granted for this research. K.M. was also supported by SAMRC and SAQuTi funding.
A
Clinical Trial Registration. Not applicable. This study is not a clinical trial.
Ethics Declaration
Not applicable.
Consent to Publish:
Not applicable.
Consent to Participate:
Not applicable.
Abbreviations
HIV
Human immunodeficiency virus
PBS
Phosphate buffered saline
3MPTS − 3
Mercaptopropyltrimethoxysilane
GMBS
N-(4-Maleimidobutyryloxy) sulfosuccinimide
LFMC
Lateral flow microfluidic chip
References
1.
Song Y, Zhao X, Tian Q, Liang H (2018) Fundamental Concepts and Physics in Microfluidics, in Microfluidics: Fundamental, Devices and Applications, 10.1002/9783527800643.ch2
2.
Regmi S, Poudel C, Adhikari R, Luo KQ (2022) Applications of Microfluidics and Organ-on-a-Chip in Cancer Research. Biosens (Basel) 12(7). 10.3390/bios12070459
3.
Anushka A, Bandopadhyay, Das PK (2023) Paper based microfluidic devices: a review of fabrication techniques and applications. 10.1140/epjs/s11734-022-00727-y
4.
Sekhwama M, Mpofu K, Sivarasu S, Mthunzi-Kufa P (2024) Applications of microfluidics in biosensing. Discover Appl Sci 6(6):303. 10.1007/s42452-024-05981-4
5.
Preetam S et al (2022) Emergence of microfluidics for next generation biomedical devices. Biosens Bioelectron X 10. 10.1016/j.biosx.2022.100106
6.
Li X et al (2022) Application of Microfluidics in Drug Development from Traditional Medicine. 10.3390/bios12100870
7.
Shi Y et al (2021) Application of Microfluidics in Immunoassay: Recent Advancements. 10.1155/2021/2959843
8.
Sekhwama M, Mpofu K, Sudesh S, Mthunzi-Kufa P (2024) Integration of microfluidic chips with biosensors. Discover Appl Sci 6:458. 10.1007/s42452-024-06103-w
9.
Scott SM, Ali Z (2021) Fabrication methods for microfluidic devices: An overview. 10.3390/mi12030319
10.
Kim JJ, Bae M, Kim J, Cho DW (2023) Application of biomaterial-based threedimensional bioprinting for organ-on-a-chip fabrication. Int J Bioprint 10(1). 10.36922/ijb.1972
11.
Cao UMN, Zhang Y, Chen J, Sayson D, Pillai S, Tran SD (2023) Microfluidic Organ-on-A-chip: A Guide to Biomaterial Choice and Fabrication. 10.3390/ijms24043232
12.
Sekhwama M, Mpofu K, Mthunzi-Kufa P (2023) Using 3D printing to fabricate microfluidic chips for biosensing applications, MATEC Web of Conferences, vol. 388, p. 05001. 10.1051/matecconf/202338805001
13.
Sekhwama Masindi M, Kelvin M, Phumulani S, Sudesh, Patience M-K (2024) 3D printed microfluidic chip design for diagnostic studies. MATEC Web Conf 406:8004. 10.1051/matecconf/202440608004
14.
Su R, Wang F, McAlpine MC (2023) 3D printed microfluidics: advances in strategies, integration, and applications. 10.1039/d2lc01177h
15.
Sekhwama M, Mpofu K, Mthunzi-Kufa P (2023) Using 3D printing to fabricate microfluidic chips for biosensing applications, MATEC Web of Conferences, vol. 388. 10.1051/matecconf/202338805001
16.
Bezelya A, Küçüktürkmen B, Bozkır A (2023) Microfluidic Devices for Precision Nanoparticle Production. Micro 3(4). 10.3390/micro3040058
17.
Katiyar NK, Goel G, Goel S (2022) Nanomaterials Based Biosensing: Methods and Principle of Detection. Mater Horizons: Nat Nanomaterials. 10.1007/978-981-16-3645-5_1
18.
Ellerbee AK et al (2009) Quantifying colorimetric assays in paper-based microfluidic devices by measuring the transmission of light through paper. Anal Chem 81(20). 10.1021/ac901307q
19.
Jia Y, Chen S, Wang Q, Li J (2024) Recent progress in biosensor regeneration techniques. 10.1039/d3nr05456j
20.
Wang B, Huang D, Weng Z (2023) Recent Advances in Polymer-Based Biosensors for Food Safety Detection. 10.3390/polym15153253
21.
Chadha U et al (2022) Recent progress and growth in biosensors technology: A critical review. 10.1016/j.jiec.2022.02.010
22.
Smutok O, Katz E (2023) Biosensors: Electrochemical Devices—General Concepts and Performance. 10.3390/bios13010044
23.
Noviana E et al (2021) Microfluidic Paper-Based Analytical Devices: From Design to Applications,., 10.1021/acs.chemrev.0c01335
24.
Ramesh M, Janani R, Deepa C, Rajeshkumar L (2023) Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications. 10.3390/bios13010040
25.
Sekhwama M, Mpofu K, Mcoyi P, Sivarasu S, Mthunzi-Kufa P (2024) Real-time detection of HIV-1 using an absorbance-based biosensor: a comparative study of surface coatings and antibody functionalization. 10.1117/12.3002322
26.
Chen YT et al (2020) Review of Integrated Optical Biosensors for Point-of-Care Applications. 10.3390/BIOS10120209
27.
John P, Vasa NJ, Zam A (2023) Optical Biosensors for the Diagnosis of COVID-19 and Other Viruses—A Review. 10.3390/diagnostics13142418
28.
Mpofu K, Chauke S, Thwala L, Mthunzi-Kufa P (2025) Aptamers and antibodies in optical biosensing. Discover Chem 2(1):23. 10.1007/s44371-025-00094-2
29.
Mostufa S et al (2024) Advancements and Perspectives in Optical Biosensors, ACS Omega, vol. 9, no. 23, pp. 24181–24202, Jun. 10.1021/acsomega.4c01872
A
30.Garzón V, Pinacho DG, Bustos RH, Garzón G, Bustamante S (2019) Optical biosensors for therapeutic drug monitoring. 10.3390/bios9040132
31.
Wang X, Yi L, Mukhitov N, Schrell AM, Dhumpa R, Roper MG (2015) Microfluidics-to-mass spectrometry: A review of coupling methods and applications. 10.1016/j.chroma.2014.10.039
32.
Buttkewitz MA, Heuer C, Bahnemann J (2023) Sensor integration into microfluidic systems: trends and challenges. 10.1016/j.copbio.2023.102978
33.
Tsebesebe N, Mpofu K, Ndlovu S, Sivarasu S, Mthunzi-Kufa P (2023) Detection of SARS-CoV-2 from raman spectroscopy data using machine learning models, MATEC Web of Conferences, vol. 388, p. 07002. 10.1051/matecconf/202338807002
34.
Mpofu KT, Lee C, Maguire GEM, Kruger HG, Tame MS (2022) Measuring kinetic parameters using quantum plasmonic sensing. Phys Rev (Coll Park) 105(3). 10.1103/PhysRevA.105.032619
35.
Mpofu KT, Lee C, Maguire GEM, Kruger HG, Tame MS (2022) Experimental measurement of kinetic parameters using quantum plasmonic sensing. J Appl Phys 131(8). 10.1063/5.0079896
36.
Mpofu KT, Mthunzi-Kufa P (2023) Enhanced signal-to-noise ratio in quantum plasmonic image sensing including loss and varying photon number. Phys Scr 98(11):115115
37.
Mpofu KT, Ombinda-Lemboumba S, Mthunzi-Kufa P (2023) Classical and Quantum Surface Plasmon Resonance Biosensing, Int J Opt, vol. 2023
38.
Mauranyapin NP, Madsen LS, Taylor MA, Waleed M, Bowen WP (2017) Evanescent single-molecule biosensing with quantum-limited precision. Nat Photonics 11(8). 10.1038/nphoton.2017.99
39.
Casacio CA et al (2021) Quantum-enhanced nonlinear microscopy. Nature 594(7862). 10.1038/s41586-021-03528-w
40.
Dowran M, Kumar A, Lawrie BJ, Pooser RC, Marino AM (2018) Quantum-enhanced plasmonic sensing. Optica 5(5). 10.1364/optica.5.000628
41.
Triginer Garces G et al (2020) Quantum-enhanced stimulated emission detection for label-free microscopy. Appl Phys Lett 117(2). 10.1063/5.0009681
42.
Lee C, Lawrie B, Pooser R, Lee KG, Rockstuhl C, Tame M (2021) Quantum Plasmonic Sensors. Chem Rev 121(8). 10.1021/acs.chemrev.0c01028
43.
Mpofu K, Chauke S, Thwala L, Mthunzi-Kufa P (2025) Aptamers and antibodies in optical biosensing. Discover Chem 2(1):23. 10.1007/s44371-025-00094-2
44.
Mpofu KT, Mthunzi-Kufa P (2025) Recent advances in artificial intelligence and machine learning based biosensing technologies. IntechOpen. 10.5772/intechopen.1009613
45.
Tsebesebe NT, Mpofu K, Sivarasu S, Mthunzi-Kufa P (Apr. 2025) Arduino-based devices in healthcare and environmental monitoring. Discover Internet Things 5(1):1–31. 10.1007/s43926-025-00139-z