Metal-Organic Frameworks/Heterojunction Structures for Surface-Enhanced Raman Scattering with Enhanced Sensitivity and Tailorability.

Metal-organic frameworks (MOFs), which are composed of crystalline microporous materials with metal ions, have gained considerable interest as promising substrate materials for surface-enhanced Raman scattering (SERS) detection via charge transfer. Research on MOF-based SERS substrates has advanced rapidly because of the MOFs' excellent structural tunability, functionalizable pore interiors, and ultrahigh surface-to-volume ratios. Compared with traditional noble metal SERS plasmons, MOFs exhibit better biocompatibility, ease of operation, and tailorability. However, MOFs cannot produce a sufficient limit of detection (LOD) for ultrasensitive detection, and therefore, developing an ultrasensitive MOF-based SERS substrate is imperative. To the best of our knowledge, this is the first study to develop an MOFs/heterojunction structure as an SERS enhancing material. We report an in situ ZIF-67/Co(OH)2 heterojunction-based nanocellulose paper (nanopaper) plate (in situ ZIF-67 nanoplate) as a device with an LOD of 0.98 nmol/L for Rhodamine 6G and a Raman enhancement of 1.43 × 107, which is 100 times better than that of the pure ZIF-67-based SERS substrate. Further, we extend this structure to other types of MOFs and develop an in situ HKUST-1 nanoplate (with HKUST-1/Cu(OH)2). In addition, we demonstrate that the formation of heterojunctions facilitates efficient photoinduced charge transfer for SERS detection by applying the Mx(OH)y-assisted (where M = Co, Cu, or other metals) MOFs/heterojunction structure. Finally, we successfully demonstrate the application of medicine screening on our nanoplates, specifically for omeprazole. The nanoplates we developed still maintain the tailorability of MOFs and perform high anti-interference ability. Our approach provides customizing options for MOF-based SERS detection, catering to diverse possibilities in future research and applications.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (µPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on µPADs can further facilitate the realization of smartphone µPADs platforms for efficient disease detection. RESULTS: This paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on µPADs. Our platform successfully applied sandwich c-ELISA for detecting the ß-amyloid peptide 1-42 (Aß 1-42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined "samples in, answers out" approach. SIGNIFICANCE: This deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aß 1-42, particularly in areas with low resources and limited communication infrastructure.

A SERS nanocellulose-paper-based analytical device for ultrasensitive detection of Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD), one of the most prevalent neurodegenerative diseases, results in severe cognitive decline and irreversible memory loss. Early detection of AD is significant to patients for personalized intervention since effective cure and treatment methods for AD are still lacking. Despite the severity of the disease, existing highly sensitive AD detection methods, including neuroimaging and brain deposit-positive lesion tests, are not suitable for screening purposes due to their high cost and complicated operation. Therefore, these methods are unsuitable for early detection, especially in low-resource settings. Although regular paper-based microfluidics are cost-efficient for AD detection, they are restricted by a poor limit of detection (LOD). RESULTS: To address the above limitations, we report the ultrasensitive and low-cost nanocellulose paper (nanopaper)-based analytical microfluidic devices (NanoPADs) for detecting one of the promising AD blood biomarkers (glial fibrillary acidic protein, GFAP) using Surface-enhanced Raman scattering (SERS) immunoassay. Nanopaper offers advantages as a SERS substrate, such as an ultrasmooth surface, high optical transparency, and tunable chemical properties. We detected the target GFAP in artificial serum, achieving a LOD of 150 fg mL-1. SIGNIFICANCE: The developed NanoPADs are distinguished by their cost-efficiency and ease of implementation, presenting a promising avenue for effective early detection of AD's GFAP biomarker with ultrahigh sensitivity. More importantly, our work provides the experimental routes for SERS-based immunoassay of biomarkers on NanoPADs for various diseases in the future.

Cellulose Nanostructures as Tunable Substrates for Nanocellulose-Metal Hybrid Flexible Composites.

Nanocomposite represents the backbone of many industrial fabrication applications and exerts a substantial social impact. Among these composites, metal nanostructures are often employed as the active constituents, thanks to their various chemical and physical properties, which offer the ability to tune the application scenarios in thermal management, energy storage, and biostable materials, respectively. Nanocellulose, as an emerging polymer substrate, possesses unique properties of abundance, mechanical flexibility, environmental friendliness, and biocompatibility. Based on the combination of flexible nanocellulose with specific metal fillers, the essential parameters involving mechanical strength, flexibility, anisotropic thermal resistance, and conductivity can be enhanced. Nowadays, the approach has found extensive applications in thermal management, energy storage, biostable electronic materials, and piezoelectric devices. Therefore, it is essential to thoroughly correlate cellulose nanocomposites' properties with different metallic fillers. This review summarizes the extraction of nanocellulose and preparation of metal modified cellulose nanocomposites, including their wide and particular applications in modern advanced devices. Moreover, we also discuss the challenges in the synthesis, the emerging designs, and unique structures, promising directions for future research. We wish this review can give a valuable overview of the unique combination and inspire the research directions of the multifunctional nanocomposites using proper cellulose and metallic fillers.

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 lies in a range of excellent qualities, including an exceptionally smooth surface, outstanding optical transparency, a uniform nanofiber matrix with nanoscale porosity, and customizable chemical properties. Despite the rapid growth of nanopaper-based microfluidics, the current techniques used to create microchannels on nanopaper, such as 3D printing, spray coating, or manual cutting and assembly, which are crucial for practical applications, still possess certain limitations, notably susceptibility to contamination. Furthermore, these methods are restricted to the production of millimeter-sized channels. This study introduces a straightforward process that utilizes convenient plastic micro-molds for simple microembossing operations to fabricate microchannels on nanopaper, achieving a minimum width of 200 µm. The developed microchannel outperforms existing approaches, achieving a fourfold improvement, and can be fabricated within 45 min. Furthermore, fabrication parameters have been optimized, and a convenient quick-reference table is provided for application developers. The proof-of-concept for a laminar mixer, droplet generator, and functional nanopaper-based analytical devices (NanoPADs) designed for Rhodamine B sensing using surface-enhanced Raman spectroscopy was demonstrated. Notably, the NanoPADs exhibited exceptional performance with improved limits of detection. These outstanding results can be attributed to the superior optical properties of nanopaper and the recently developed accurate microembossing method, enabling the integration and fine-tuning of the NanoPADs.

Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7.

The Caenorhabditis elegans (C. elegans) 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 C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans 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 C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans 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.

Microfluidic-Assisted Caenorhabditis elegans Sorting: Current Status and Future Prospects.

Caenorhabditis elegans (C. elegans) 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 C. elegans 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 C. elegans 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 C. elegans, and are hard to read for worm researchers. We aim to comprehensively review the up-to-date microfluidic-assisted C. elegans sorting developments from several angles to suit different background researchers, i.e., biologists and engineers. First, we highlighted the microfluidic C. elegans 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.

An ultrasensitive FET biosensor based on vertically aligned MoS2 nanolayers with abundant surface active sites.

Molybdenum disulfide (MoS2) 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 MoS2, vertically-aligned MoS2 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 MoS2 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-1). 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.

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

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.

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

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.

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

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.

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

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 (~1013 cm-2) 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.