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Microembossing: A Convenient Process for Fabricating Microchannels on Nanocellulose Paper-Based Microfluidics

Nanopaper, derived from nanofibrillated cellulose, has generated considerable interest as a promising material for microfluidic applications. Its appeal ...

Xi’an Jiaotong - Liverpool University

University of Liverpool

Field-effect-transistor (FET) biosensors capable of rapidly detecting disease-relevant biomarkers have long been considered as a promising tool for point-of-care (POC) diagnosis. Rolled-up nanotechnology, as a batch fabrication strategy for generating three-dimensional (3D) microtubes, has been demonstrated to possess unique advantages for constructing FET biosensors. In this paper, we report a new approach combining the two fascinating technologies, the FET biosensor and the rolled-up microtube, to develop a microfluidic diagnostic biosensor. We integrated an excellent biosensing III-nitride material-indium nitride (InN)-into a rolled-up microtube and used it as the FET channel. The InN possesses strong, intrinsic, and stable electron accumulation (~10 cm) on its surface, thereby providing a high device sensitivity. Multiple rolled-up InN microtube FET biosensors fabricated on the same substrate were integrated with a microfluidic channel for convenient fluids handling, and shared the same external electrode (inserted into the microchannel outlet) for gating voltage modulation. Using human immunodeficiency virus (HIV) antibody as a model disease marker, we characterized the analytical performance of the developed biosensor and achieved a limit of detection (LOD) of 2.5 pM for serum samples spiked with HIV gp41 antibodies. The rolled-up InN microtube FET biosensor represents a new type of III-nitride-based FET biosensor and holds significant potential for practical POC diagnosis.

A microfluidic field-effect transistor biosensor with rolled-up indium nitride microtubes.

The 2020 COVID-19 pandemic has greatly accelerated the adoption of online learning and teaching in many colleges and universities. Video, as a key integral part of online education, largely influences student learning experiences. Though many guidelines on designing educational videos have been reported, the quantitative data showing the impacts of video length on students' academic performance in a credit-bearing course is limited, particularly for an online-flipped college engineering course. The forced pandemic lockdown enables a suitable environment to address this research gap. In this paper, we present the first step to examine the impact of short videos on students' academic performance in such circumstances. Our results indicate that short videos can greatly improve student engagement by 24.7% in terms of video viewing time, and the final exam score by 9.0%, both compared to the long-video group. The quantitative Likert questionnaire also indicates students' preference for short videos over long videos. We believe this study has important implications for course design for future online-flipped engineering courses.

The impact of short videos on student performance in an online-flipped college engineering course.

Nanofibrillated cellulose paper (nanopaper) has gained growing interest as one promising substrate material for paper-based microfluidics, thanks to its ultrasmooth surface, high optical transparency, uniform nanofiber matrix with nanoscale porosity, and tunable chemical properties. Recently, research on nanopaper-based microfluidics has quickly advanced; however, the current technique of patterning microchannels on nanopaper (i.e., 3D printing, spray coating, or manual cutting and sticking), that is fundamental for application development, still has some limitations, such as ease-of-contamination, and more importantly, only enabling millimeter-scale channels. This paper reports a facile process that leverages the simple operations of microembossing with the convenient plastic micro-molds, for the first time, patterning nanopaper microchannels downing to 200 μm, which is 4 times better than the existing methods and is time-saving (<45 mins). We also optimized the patterning parameters and provided one quick look-up table as the guideline for application developments. As proof-of-concept, we first demonstrated two fundamental microfluidic devices on nanopaper, the laminar-mixer and droplet generator, and two functional nanopaper-based analytical devices (NanoPADs) for glucose and Rhodamine B (RhB) sensing based on optical colorimetry and surface-enhanced Raman spectroscopy, respectively. The two NanoPADs showed outstanding performance with low limits of detection (2 mM for glucose and 19fM for RhB), which are 1.25× and 500× fold improvement compared to the previously reported values. This can be attributed to our newly developed highly accurate microchannel patterning process that enables high integration and fine-tunability of the NanoPADs along with the superior optical properties of nanopaper.

Facile Microembossing Process for Microchannel Fabrication for Nanocellulose-Paper-Based Microfluidics.

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs.

Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays.

Molybdenum disulfide (MoS) nanolayers are one of the most promising two-dimensional (2D) nanomaterials for constructing next-generation field-effect transistor (FET) biosensors. In this article, we report an ultrasensitive FET biosensor that integrates a novel format of 2D MoS, vertically-aligned MoS nanolayers (VAMNs), as the channel material for label-free detection of the prostate-specific antigen (PSA). The developed VAMNs-based FET biosensor shows two distinctive advantages. First, the VAMNs can be facilely grown using the conventional chemical vapor deposition (CVD) method, permitting easy fabrication and potential mass device production. Second, the unique advantage of the VAMNs for biosensor development lies in its abundant surface-exposed active edge sites that possess a high binding affinity with thiol-based linkers, which overcomes the challenge of molecule functionalization on the conventional planar MoS nanolayers. The high binding affinity between 11-mercaptoundecanoic acid and the VAMNs was demonstrated through experimental surface characterization and theoretical calculations via density functional theory. The FET biosensor allows rapid (within 20 min) and ultrasensitive PSA detection in human serum with simple operations (limit of detection: 800 fg mL). This FET biosensor offers excellent features such as ultrahigh sensitivity, ease of fabrication, and short assay time, and thereby possesses significant potential for early-stage diagnosis of life-threatening diseases.

An ultrasensitive FET biosensor based on vertically aligned MoS nanolayers with abundant surface active sites.

 () has been a popular model organism for several decades since its first discovery of the huge research potential for modeling human diseases and genetics. Sorting is an important means of providing stage- or age-synchronized worm populations for many worm-based bioassays. However, conventional manual techniques for  sorting are tedious and inefficient, and commercial complex object parametric analyzer and sorter is too expensive and bulky for most laboratories. Recently, the development of lab-on-a-chip (microfluidics) technology has greatly facilitated  studies where large numbers of synchronized worm populations are required and advances of new designs, mechanisms, and automation algorithms. Most previous reviews have focused on the development of microfluidic devices but lacked the summaries and discussion of the biological research demands of , and are hard to read for worm researchers. We aim to comprehensively review the up-to-date microfluidic-assisted  sorting developments from several angles to suit different background researchers, i.e., biologists and engineers. First, we highlighted the microfluidic  sorting devices' advantages and limitations compared to the conventional commercialized worm sorting tools. Second, to benefit the engineers, we reviewed the current devices from the perspectives of active or passive sorting, sorting strategies, target populations, and sorting criteria. Third, to benefit the biologists, we reviewed the contributions of sorting to biological research. We expect, by providing this comprehensive review, that each researcher from this multidisciplinary community can effectively find the needed information and, in turn, facilitate future research.

Microfluidic-Assisted  Sorting: Current Status and Future Prospects.

The  is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of  for experiments is tedious and inefficient. The microfluidic-assisted  sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated  sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize  automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated  identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union (mAP@0.5) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an mAP@0.5 of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms.

Article	Year
A microfluidic field-effect transistor biosensor with rolled-up indium nitride microtubes. Biosensors & bioelectronics\| PubMed ID: 34225055	2021
The impact of short videos on student performance in an online-flipped college engineering course. Humanities & social sciences communications\| PubMed ID: 36187843	2022
Facile Microembossing Process for Microchannel Fabrication for Nanocellulose-Paper-Based Microfluidics. ACS applied materials & interfaces\| PubMed ID: 36693010	2023
Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays. Analytica chimica acta\| PubMed ID: 36813452	2023
An ultrasensitive FET biosensor based on vertically aligned MoS nanolayers with abundant surface active sites. Analytica chimica acta\| PubMed ID: 36935147	2023
Microfluidic-Assisted Sorting: Current Status and Future Prospects. Cyborg and bionic systems (Washington, D.C.)\| PubMed ID: 37287459	2023
Deep Learning for Microfluidic-Assisted Multi-Parameter Identification Using YOLOv7. Micromachines\| PubMed ID: 37512650	2023

Sixuan Duan

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