Multichannel Hierarchical Analysis of Time-Resolved Hyperspectral Data for Advanced Colorimetric E-Nose.

The colorimetric sensor-based electronic nose has been demonstrated to discriminate specific gaseous molecules for various applications, including health or environmental monitoring. However, conventional colorimetric sensor systems rely on RGB sensors, which cannot capture the complete spectral response of the system. This limitation can degrade the performance of machine learning analysis, leading to inaccurate identification of chemicals with similar functional groups. Here, we propose a novel time-resolved hyperspectral (TRH) data set from colorimetric array sensors consisting of 1D spatial, 1D spectral, and 1D temporal axes, which enables hierarchical analysis of multichannel 2D spectrograms via a convolution neural network (CNN). We assessed the outstanding classification performance of the TRH data set compared to an RGB data set by conducting a relative humidity (RH) concentration classification. The time-dependent spectral response of the colorimetric sensor was measured and trained as a CNN model using TRH and RGB sensor systems at different RH levels. While the TRH model shows a high classification accuracy of 97.5% for the RH concentration, the RGB model yields 72.5% under identical conditions. Furthermore, we demonstrated the detection of various functional volatile gases with the TRH system by using experimental and simulation approaches. The results reveal distinct spectral features from the TRH system, corresponding to changes in the concentration of each substance.

3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer.

The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.

Non-intrusive quality appraisal of differentiation-induced cardiovascular stem cells using E-Nose sensor technology.

Stem cell technology holds immense potential for revolutionizing medicine, particularly in regenerative treatment for heart disease. The unique capacity of stem cells to differentiate into diverse cell types offers promise in repairing damaged tissues and implanting organs. Ensuring the quality of differentiated cells, essential for specific functions, demands in-depth analysis. However, this process consumes time and incurs substantial costs while invasive methods may alter stem cell features during differentiation and deplete cell numbers. To address these challenges, we propose a non-invasive strategy, using cellular respiration, to assess the quality of differentiation-induced stem cells, notably cardiovascular stem cells. This evaluation employs an electronic nose (E-Nose) and neural pattern separation (NPS). Our goal is to assess differentiation-induced cardiac stem cells (DICs) quality through E-Nose data analysis and compare it with standard commercial human cells (SCHCs). Sensitivity and specificity were evaluated by interacting SCHCs and DICs with the E-Nose, achieving over 90% classification accuracy. Employing selective combinations optimized by NPS, E-Nose successfully classified all six cell types. Consequently, the relative similarity among DICs like cardiomyocytes, endothelial cells with SCHCs was established relied on comparing response data from the E-Nose sensor without resorting to complex evaluations.

Induction of Browning in White Adipocytes: Fucoidan Characterization and Gold Nanoparticle Synthesis from Undaria pinnatifida Sporophyll Extract.

Seaweed extracts and their specific polysaccharides are widely known for their ability to act as reducing and capping agents during nanoparticle synthesis. Their application is highly favored in green synthesis methods, owing to their eco-friendliness, cost-effectiveness, and remarkable time and energy efficiency. In this study, fucoidan extracted from Undaria pinnatifida sporophyll (UPS) is introduced as a polysaccharide that effectively serves as a dual-function reducing and capping agent for the synthesis of gold nanoparticles (AuNPs). Results from various analyses indicate that AuNPs derived from UPS extract display a uniform spherical shape with an average size of 28.34 ± 1.15 nm and a zeta potential of -37.49 ± 2.13 mV, conclusively confirming the presence of Au. The FT-IR spectra distinctly revealed the characteristic fucoidan bands on the stabilized UPS-AuNPs surface. A 1H-NMR analysis provided additional confirmation by revealing the presence of specific fucoidan protons on the UPS-AuNPs surface. To comprehensively evaluate the impact of UPS extract, UPS-AuNPs, and fucoidan on the biological properties of adipocytes, a rigorous comparative analysis of lipid droplet formation and morphology was conducted. Our findings revealed that adipocytes treated with UPS extract, fucoidan, and UPS-AuNPs, in that order, exhibited a reduction in the total lipid droplet surface area, maximum Ferret diameter, and overall Nile red staining intensity when compared to mature white adipocytes. Furthermore, our analysis of the effects of UPS extracts, UPS-AuNPs, and fucoidan on the expression of key markers associated with white adipose tissue browning, such as UCP1, PGC1a, and PRDM16, demonstrated increased mRNA and protein expression levels in the following order: UPS-AuNPs > fucoidan > UPS extracts. Notably, the production of active mitochondria, which play a crucial role in enhancing energy expenditure in beige adipocytes, also increased in the following order: UPS-AuNPs > fucoidan > UPS extract. These findings underscore the pivotal role of UPS extract, fucoidan, and UPS-AuNPs in promoting adipocyte browning and subsequently enhancing energy expenditure.

Roll-to-plate 0.1-second shear-rolling process at elevated temperature for highly aligned nanopatterns.

The shear-rolling process is a promising directed self-assembly method that can produce high-quality sub-10 nm block copolymer line-space patterns cost-effectively and straightforwardly over a large area. This study presents a high temperature (280 °C) and rapid (~0.1 s) shear-rolling process that can achieve a high degree of orientation in a single process while effectively preventing film delamination, that can be applied to large-area continuous processes. By minimizing adhesion, normal forces, and ultimate shear strain of the polydimethylsiloxane pad, shearing was successfully performed without peeling up to 280 °C at which the chain mobility significantly increases. This method can be utilized for various high-χ block copolymers and surface neutralization processes. It enables the creation of block copolymer patterns with a half-pitch as small as 8 nm in a unidirectional way. Moreover, the 0.1-second rapid shear-rolling was successfully performed on long, 3-inch width polyimide flexible films to validate its potential for the roll-to-roll process.

Fountain Pen-Inspired 3D Colloidal Assembly, Consisting of Metallic Nanoparticles on a Femtoliter Scale.

The 3D colloidal assemblies composed of nanoparticles (NPs) are closely associated with optical properties such as photonic crystals, localized surface plasmon resonance, and surface-enhanced Raman scattering. However, research on their fabrication remains insufficient. Here, the femtoliter volume of a 3D colloidal assembly is shown, using the evaporation of a fine fountain pen. A nano-fountain pen (NPF) with a micrometer-level tip inner diameter was adopted for the fine evaporation control of the ink solvent. The picoliters of the evaporation occurring at the NFP tip and femtoliter volume of the 3D colloidal assembly were analyzed using a diffusion equation. The shape of the 3D colloidal assembly was dependent on the evaporation regarding the accumulation time and tip size, and they exhibited random close packing. Using gold-, silver-, and platinum-NPs and mixing ratios of them, diverse 3D colloidal assemblies were formed. The spectra regarding a localized surface plasmon resonance of them were changed according to composition and mixing ratio. We expect that this could be widely applied as a simple fabrication tool in order to explore complex metamaterials constructed of nanoparticles, as this method is highly flexible in varying the shape as well as composition ratio of self-assembled structures.

Sensor development for multiple simultaneous classifications using genetically engineered M13 bacteriophages.

Sensors for detecting infinitesimal amounts of chemicals in air have been widely developed because they can identify the origin of chemicals. These sensing technologies are also used to determine the variety and freshness of fresh food and detect explosives, hazardous chemicals, environmental hormones, and diseases using exhaled gases. However, there is still a need to rapidly develop portable and highly sensitive sensors that respond to complex environments. Here, we show an efficient method for optimising an M13 bacteriophage-based multi-array colourimetric sensor for multiple simultaneous classifications. Apples, which are difficult to classify due to many varieties in distribution, were selected for classifying targets. M13 was adopted to fabricate a multi-array colourimetric sensor using the self-templating process since a chemical property of major coat protein p8 consisting of the M13 body can be manipulated by genetic engineering to respond to various target substances. The twenty sensor units, which consisted of different types of manipulated M13, exhibited colour changes because of the change of photonic crystal-like nanostructure when they were exposed to target substances associated with apples. The classification success rate of the optimal sensor combinations was achieved with high accuracy for the apple variety (100%), four standard fragrances (100%), and aging (84.5%) simultaneously. We expect that this optimisation technique can be used for rapid sensor development capable of multiple simultaneous classifications in various fields, such as medical diagnosis, hazardous environment monitoring, and the food industry, where sensors need to be developed in response to complex environments consisting of various targets.

OGD/R-induced ferroptosis and pyroptosis in retinal pigment epithelium cells: Role of PLD1 and PLD2 modulation.

This study investigated the role of phospholipase D (PLD) in retinal ischemia-reperfusion (I/R) injury using an oxygen-glucose deprivation/reperfusion (OGD/R) model commonly used in retinal I/R injury research. To create an in vitro cellular I/R model, pharmacological inhibitors and small interfering RNA (siRNA) were used to target PLD1 and PLD2 in retinal pigment epithelial (RPE) cells. Treatment with PLD inhibitors and siRNA reduced reactive oxygen species (ROS) and malondialdehyde (MDA) induced by OGD/R in RPE cells and increased the levels of superoxide dismutase (SOD) and glutathione (GSH), indicating a reduction in oxidative damage and improvement in the antioxidant system. Next, we showed that inhibiting PLD1 or PLD2 reduced intracellular iron levels and lipid peroxidation, which are critical factors in ferroptosis. Additionally, PLD1 and PLD2 modulated the expression of proteins involved in the regulation of ferroptosis, including GPX4, SLC7A11, FTH1, and ACSL4. We also investigated the roles of PLD1 and PLD2 in preventing pyroptosis, another form of programmed cell death associated with inflammation. Our study found that OGD/R significantly increased the production of pro-inflammatory cytokines and activated caspase-1, NLRP3, ASC, cleaved-caspase 1 (C-caspase-1), and GSDMD-N in RPE cells, indicating pyroptosis induction. However, PLD1 and PLD2 inhibition or knockdown significantly inhibited the production of pro-inflammatory cytokines and activation of the NLRP3 inflammasome, Taken together, our findings support the hypothesis that the PLD signaling pathway plays a key role in OGD/R-induced ferroptosis and pyroptosis induction and may be a potential therapeutic target for preventing or treating retinal dysfunction and degeneration.

Freestanding, Freeform Metamolecule Fibers Tailoring Artificial Optical Magnetism.

Metamolecule clusters support various unique types of artificial electromagnetism at optical frequencies. However, the technological challenges regarding the freeform fabrication of freestanding metamolecule clusters with programmed geometries and multiple compositions remain unresolved. Here, the freeform, freestanding raspberry-like metamolecule (RMM) fibers based on the directional guidance of a femtoliter meniscus are presented, resulting in the evaporative co-assembly of silica nanoparticles and gold nanoparticles with the aid of 3D nanoprinting. This method offers a facile and universal pathway to shape RMM fibers in 3D, enabling versatile manipulation of near- and far-field characteristics. In particular, the authors demonstrate the ability to decrease the scattering of the millimeter-scale RMM fiber in visible spectrum. In addition, the influence of electric and magnetic dipole modes on the directional scattering of RMM fibers is investigated. These experiments show that the magnetic response of an individual RMM can be controlled by adjusting the filling factor of gold nanoparticles. The authors anticipate that this method will allow for unrestricted design and realization of nanophotonic structures, surpassing the limitations of conventional fabrication processes.

Understanding the Role of M13 Bacteriophage Thin Films on a Metallic Nanostructure through a Standard and Dynamic Model.

The dynamic and surface manipulation of the M13 bacteriophage via the meeting application demands the creation of a pathway to design efficient applications with high selectivity and responsivity rates. Here, we report the role of the M13 bacteriophage thin film layer that is deposited on an optical nanostructure involving gold nanoparticles/SiO2/Si, as well as its influence on optical and geometrical properties. The thickness of the M13 bacteriophage layer was controlled by varying either the concentration or humidity exposure levels, and optical studies were conducted. We designed a standard and dynamic model based upon three-dimensional finite-difference time-domain (3D FDTD) simulations that distinguished the respective necessity of each model under variable conditions. As seen in the experiments, the origin of respective peak wavelength positions was addressed in detail with the help of simulations. The importance of the dynamic model was noted when humidity-based experiments were conducted. Upon introducing varied humidity levels, the dynamic model predicted changes in plasmonic properties as a function of changes in NP positioning, gap size, and effective index (this approach agreed with the experiments and simulated results). We believe that this work will provide fundamental insight into understanding and interpreting the geometrical and optical properties of the nanostructures that involve the M13 bacteriophage. By combining such significant plasmonic properties with the numerous benefits of M13 bacteriophage (like low-cost fabrication, multi-wavelength optical characteristics devised from a single structure, reproducibility, reversible characteristics, and surface modification to suit application requirements), it is possible to develop highly efficient integrated plasmonic biomaterial-based sensor nanostructures.

Artificial Neural Processing-Driven Bioelectronic Nose for the Diagnosis of Diabetes and Its Complications.

Diabetes and its complications affect the younger population and are associated with a high mortality rate; however, early diagnosis can contribute to the selection of appropriate treatment regimens that can reduce mortality. Although diabetes diagnosis via exhaled breath has great potential for early diagnosis, research on such diagnosis is restricted to disease detection, requiring in-depth examination to diagnose and classify diseases and their complications. This study demonstrates the use of an artificial neural processing-based bioelectronic nose to accurately diagnose diabetes and classify diabetic types (type I and II) and their complications, such as heart disease. Specifically, an M13 phage-based electronic nose (e-nose) is used to explore the features of subjects with diabetes at various levels of cellular and organismal organization (cells, liver organoids, and mice). Exhaled breath samples are collected during culturing and exposed to the phage-based e-nose. Compared with cells, liver organoids cultured under conditions mimicking a diabetic environment display properties that closely resemble the characteristics of diabetic mice. Using neural pattern separation, the M13 phage-based e-nose achieves a classification success rate of over 86% for four conditions in mice, namely, type 1 diabetes, type 2 diabetes, diabetic cardiomyopathy, and cardiomyopathy.

The development progress of multi-array colourimetric sensors based on the M13 bacteriophage.

Techniques for detecting chemicals dispersed at low concentrations in air continue to evolve. These techniques can be applied not only to manage the quality of agricultural products using a post-ripening process but also to establish a safety prevention system by detecting harmful gases and diagnosing diseases. Recently, techniques for rapid response to various chemicals and detection in complex and noisy environments have been developed using M13 bacteriophage-based sensors. In this review, M13 bacteriophage-based multi-array colourimetric sensors for the development of an electronic nose is discussed. The self-templating process was adapted to fabricate a colour band structure consisting of an M13 bacteriophage. To detect diverse target chemicals, the colour band was utilised with wild and genetically engineered M13 bacteriophages to enhance their sensing abilities. Multi-array colourimetric sensors were optimised for application in complex and noisy environments based on simulation and deep learning analysis. The development of a multi-array colourimetric sensor platform based on the M13 bacteriophage is likely to result in significant advances in the detection of various harmful gases and the diagnosis of various diseases based on exhaled gas in the future.

Multiarray Biosensor for Diagnosing Lung Cancer Based on Gap Plasmonic Color Films.

Adaptable and sensitive materials are essential for the development of advanced sensor systems such as bio and chemical sensors. Biomaterials can be used to develop multifunctional biosensor applications using genetic engineering. In particular, a plasmonic sensor system using a coupled film nanostructure with tunable gap sizes is a potential candidate in optical sensors because of its simple fabrication, stability, extensive tuning range, and sensitivity to small changes. Although this system has shown a good ability to eliminate humidity as an interferant, its performance in real-world environments is limited by low selectivity. To overcome these issues, we demonstrated the rapid response of gap plasmonic color sensors by utilizing metal nanostructures combined with genetically engineered M13 bacteriophages to detect volatile organic compounds (VOCs) and diagnose lung cancer from breath samples. The M13 bacteriophage was chosen as a recognition element because the structural protein capsid can readily be modified to target the desired analyte. Consequently, the VOCs from various functional groups were distinguished by using a multiarray biosensor based on a gap plasmonic color film observed by hierarchical cluster analysis. Furthermore, the lung cancer breath samples collected from 70 healthy participants and 50 lung cancer patients were successfully classified with a high rate of over 89% through supporting machine learning analysis.

Cucurbitacin B-, E-, and I-Induced Browning of White Adipocytes Is Promoted by the Inhibition of Phospholipase D2.

The mechanism of white adipose tissue browning is not well understood; however, naturally occurring compounds are known to play a positive role. The effects of cucurbitacins B, E, and I on the browning of mature white adipocytes were investigated. First, the cell viability exhibited by cucurbitacins B, E, and I in pre- and mature adipocytes was verified. Cucurbitacins B, E, and I had no effect on cell viability in pre- and mature adipocytes at concentrations up to 300 nM. To investigate the characteristics of representative beige adipocytes, the formation and morphology of cucurbitacin B, E, and I lipid droplets were verified. The total lipid droplet surface area, maximum Feret diameter, and total Nile red staining intensity of cucurbitacin B-, E-, and I-treated adipocytes were lower than those of mature white adipocytes. Furthermore, treatment of white mature adipocytes with cucurbitacin B, E, and I led to the formation of several small lipid droplets that are readily available for energy expenditure. We evaluated the effect of cucurbitacins B, E, and I on the expression of representative browning markers UCP1, PGC1a, and PRDM16, which participate in the browning of white adipose tissue. Cucurbitacins B, E, and I increased the mRNA and protein expression levels of UCP1, PGC1a, and PRDM16 in a concentration-dependent manner. To promote energy consumption by beige adipocytes, active mitochondrial biogenesis is essential. Next, we investigated the effects of cucurbitacin B, E, and I on mitochondrial biogenesis in mature adipocytes. Mitochondrial mass increased when mature adipocytes were treated with cucurbitacin B, E, and I. The degree of cucurbitacin B-, E- and I-induced transformation of white adipocytes into beige adipocytes was in the order of Cu E > Cu B > Cu I. To verify the effect of phospholipase D2 on the browning of white adipocytes, CAY10594­a PLD2 pharmacological inhibitor, and a knockdown system were used. PLD2 inhibition and knockdown improved the expression levels of UCP1, PGC1a, and PRDM16. In addition, PLD2 inhibition and knockdown in mature white adipocytes promoted mitochondrial biosynthesis. The effect of PLD2 inhibition and knockdown on promoting browning of white adipocytes significantly increased when Cu B, Cu E, and Cu I were co-treated. These data indicate that mature white adipocytes' beige properties were induced by cucurbitacins B, E, and I. These effects became more potent by the inhibition of PLD2. These findings provide a model for determining anti-obesity agents that induce browning and increase energy expenditure in mature white adipocytes.

Three-dimensional plasmonic nanoclusters driven by co-assembly of thermo-plasmonic nanoparticles and colloidal quantum dots.

Metallic nanoparticles that support localized surface plasmons have emerged as fundamental iconic building blocks for nanoscale photonics. Self-assembled clustering of plasmonic nanoparticles with controlled near-field interactions offers an interesting novel route to manipulate the electromagnetic fields at a subwavelength scale. Various bottom-up, self-assembly manners have been successfully devised to build plasmonic nanoparticle clusters displaying attractive optical properties. However, the incapability to configure on-demand architectures limits its practical reliability uses for scalable nanophotonic devices. Furthermore, a critical challenge has been addressing the accurate positioning of functional nanoparticles, including catalytic nanoparticles, dielectric nanoparticles, and quantum dots (QDs) in the clustered plasmonic hotspots. This work proposes a micropipette-based self-assembly method to fabricate three-dimensional architectures composed of colloidal clusters. The heterogeneous colloidal clusters comprising metallic nanoparticles and QDs are fabricated in one step by the micropipette-based self-assembly method. A plasmonic clustered pillar embedding QDs exhibited excellent photoluminescence characteristics compared to a collapsed pillar. The experimental and theoretical demonstration of the localized surface plasmon resonance and thermo-plasmonic properties of the colloidal clusters was performed.

Artificial olfactory sensor technology that mimics the olfactory mechanism: a comprehensive review.

Artificial olfactory sensors that recognize patterns transmitted by olfactory receptors are emerging as a technology for monitoring volatile organic compounds. Advances in statistical processing methods and data processing technology have made it possible to classify patterns in sensor arrays. Moreover, biomimetic olfactory recognition sensors in the form of pattern recognition have been developed. Deep learning and artificial intelligence technologies have enabled the classification of pattern data from more sensor arrays, and improved artificial olfactory sensor technology is being developed with the introduction of artificial neural networks. An example of an artificial olfactory sensor is the electronic nose. It is an array of various types of sensors, such as metal oxides, electrochemical sensors, surface acoustic waves, quartz crystal microbalances, organic dyes, colorimetric sensors, conductive polymers, and mass spectrometers. It can be tailored depending on the operating environment and the performance requirements of the artificial olfactory sensor. This review compiles artificial olfactory sensor technology based on olfactory mechanisms. We introduce the mechanisms of artificial olfactory sensors and examples used in food quality and stability assessment, environmental monitoring, and diagnostics. Although current artificial olfactory sensor technology has several limitations and there is limited commercialization owing to reliability and standardization issues, there is considerable potential for developing this technology. Artificial olfactory sensors are expected to be widely used in advanced pattern recognition and learning technologies, along with advanced sensor technology in the future.

Characterization of Plocamium telfairiae Extract-Functionalized Au Nanostructures and Their Anti-Adipogenic Activity through PLD1.

Here, Au nanostructure (AuNS) biosynthesis was mediated through ethanolic extract of Plocamium telfairiae (PT) without the use of stabilizers or surfactants. PT-functionalized AuNSs (PT-AuNSs) were analyzed using ultraviolet-visible spectroscopy, dynamic light scattering, high-resolution transmission electron microscopy, energy-dispersive spectroscopy, and Fourier-transform infrared spectroscopy. Stable monodisperse PT-AuNSs were synthesized, with a mean size of 15.36 ± 0.10 nm and zeta potential of -35.85 ± 1.36 mV. Moreover, biosynthetic AuNPs with a face-centered structure of PT-AuNS exhibited crystalline characteristics. In addition, many functional groups playing important roles in the biological reduction of PT extracts were adsorbed on the surface of PT-AuNSs. Furthermore, the effects of PT-AuNSs on adipogenesis in immature adipocytes were investigated. PT-AuNSs reduced morphological changes, lowered triglyceride content, and increased lipid accumulation by approximately 78.6% in immature adipocytes compared with the values in mature adipocytes (MDI-induced). PT-AuNS suppressed lipid accumulation by downregulating the transcript and protein expression of C/EBPα, PPARγ, SREBP 1, FAS, and aP2. Finally, PT-AuNS induced the transcript and protein expression of UCP1, PRDM16, and PGC1a, thereby increasing mitochondrial biogenesis in mature adipocytes and effectively inducing brown adipogenesis. In this study, the biosynthesized PT-AuNS was used as a potential therapeutic candidate because it conferred a potent anti-lipogenic effect. As a result, it can be used in various scientific fields such as medicine and the environment.

Three-Dimensional Plasmonic Nanocluster-Driven Light-Matter Interaction for Photoluminescence Enhancement and Picomolar-Level Biosensing.

Plasmonic nanoparticle clusters promise to support unique engineered electromagnetic responses at optical frequencies, realizing a new concept of devices for nanophotonic applications. However, the technological challenges associated with the fabrication of three-dimensional nanoparticle clusters with programmed compositions remain unresolved. Here, we present a novel strategy for realizing heterogeneous structures that enable efficient near-field coupling between the plasmonic modes of gold nanoparticles and various other nanomaterials via a simple three-dimensional coassembly process. Quantum dots embedded in the plasmonic structures display ∼56 meV of a blue shift in the emission spectrum. The decay enhancement factor increases as the total contribution of radiative and nonradiative plasmonic modes increases. Furthermore, we demonstrate an ultracompact diagnostic platform to detect M13 viruses and their mutations from femtoliter volume, sub-100 pM analytes. This platform could pave the way toward an effective diagnosis of diverse pathogens, which is in high demand for handling pandemic situations.

Flexible and Transparent Electrode of Hybrid Ti3C2TX MXene-Silver Nanowires for High-Performance Quantum Dot Light-Emitting Diodes.

The development of electrodes with high conductivity, optical transparency, and reliable mechanical flexibility and stability is important for numerous solution-processed photoelectronic applications. Although transparent Ti3C2TX MXene electrodes with high conductivity are promising, their suitability for displays remains limited because of the high sheet resistance, which is caused by undesirable flake junctions and surface roughness. Herein, a flexible and transparent electrode has been fabricated that is suitable for a full-solution-processed quantum dot light-emitting diode (QLED). An MXene-silver nanowire (AgNW) hybrid electrode (MXAg) consists of a highly conductive AgNW network mixed with solution-processed MXene flakes. Efficient welding of wire-to-wire junctions with MXene flakes yields an electrode with a low sheet resistance and a high transparency of approximately 13.9 Ω sq-1 and 83.8%, respectively. By employing a thin polymer buffer layer of poly(methyl methacrylate) (PMMA), followed by mild thermal treatment, a hybrid PMMA-based MXene-AgNW (MXAg@PMMA) electrode in which the work function of an MXAg hybrid FTE physically embedded in PMMA (MXAg@PMMA) can be tuned by controlling the amount of MXene in the hybrid film facilitates the development of a high-performance solution-processed QLED that exhibits maximum external quantum and current efficiencies of approximately 9.88% and 25.8 cd/A, respectively, with excellent bending stability. This work function-tunable flexible transparent electrode based on solution-processed nanoconductors provides a way to develop emerging high-performance, wearable, cost-effective, and soft electroluminescent devices.