Simple Hybrid Research Articles

A Multifunctional Solar‐Driven Formic Acid Decomposition System Using Bimetallic Phosphides/CdS as Catalysts to Obtain H2, CO, or Syngas

A simple hybrid system is constructed using CdS as photocatalyst and transition metal phosphides as cocatalysts for selectively photocatalytic formic acid decomposition under visible light irradiation at room temperature. By adjusting FA decomposition conditions, different products of H2, CO, or syngas can be obtained according to different demands. With the assistance of bimetallic phosphides as cocatalysts, the H2 and CO production efficiencies can reach up to 138.3 and 116.1 μmol mg−1 h−1 at pH = 1.5 and 9.2, respectively. At pH = 4.4, the syngas efficiency can reach up to 306.9 μmol mg−1 h−1, including 101.4 μmol mg−1 h−1 H2 and 205.5 μmol mg−1 h−1 CO. Moreover, the present heterogeneous system also exhibits excellent photocatalytic stability and still keeps high rates after the five‐cycle reaction, indicating its potential application value.

Predicting real-time roadway pluvial flood risk: A hybrid machine learning approach coupling a graph-based flood spreading model, historical vulnerabilities, and Waze data

Urban pluvial flash flooding (PFF), driven by extreme weather and urban expansion, introduces complex challenges that arise from the dynamic interaction of rainfall hazard, road vulnerability, and traffic exposure. These three critical components must be interconnected to provide a comprehensive prediction of roadway PFF risk. Our integrated approach combines historical data and real-time Waze flood alerts using a simplified physics-based PFF model and hybrid machine learning methods to predict flash flooding risk at the road segment scale. In a Dallas case study with four intersections, we trained multiple models with data from 15 storms and tested on 5 storms. The XGBoost method excels in test precision, while a Random Forest model offers better recall, and both methods outperform Support Vector Machines (SVM). The choice between models depends on factors such as negative class (prediction of unflooded areas) uncertainty and false positive cost (i.e., predicting no flooding incorrectly). For the case study, our approach could boost flood awareness, enhance safety, and improve urban flood management by correctly predicting 73% of risk observations during the test storm events.

Bifunctional binder-free ZnCuSe2 nanostructures/carbon fabric-based triboelectric nanogenerator and supercapacitor for self-charging hybrid power system application

Herein, we report a simple self-charging hybrid power system (SCHPS) based on binder-free zinc copper selenide nanostructures (ZnCuSe2 NSs) deposited carbon fabric (CF) (i.e., ZnCuSe2/CF), which is used as an active material in the fabrication of supercapacitor (SC) and triboelectric nanogenerator (TENG). At first, a binder-free ZnCuSe2/CF was synthesized via a simple and facial hydrothermal synthesis approach, and the electrochemical properties of the obtained ZnCuSe2/CF were evaluated by fabricating a symmetric quasi-solid-state SC (SQSSC). The ZCS-2 (Zn:Cu ratio of 2:1) material deposited CF-based SQSSC exhibited good electrochemical properties, and the obtained maximum energy and power densities were 7.5 Wh kg–1 and 683.3 W kg–1, respectively with 97.6 % capacitance retention after 30,000 cycles. Furthermore, the ZnCuSe2/CF was coated with silicone rubber elastomer using a doctor blade technique, which is used as a negative triboelectric material in the fabrication of the multiple TENG (M-TENG). The fabricated M-TENG exhibited excellent electrical output performance, and the robustness and mechanical stability of the device were studied systematically. The practicality and applicability of the proposed M-TENG and SQSSC were systematically investigated by powering various low-power portable electronic components. Finally, the SQSSC was combined with the M-TENG to construct a SCHPS. The fabricated SCHPS provides a feasible solution for sustainable power supply, and it shows great potential in self-powered portable electronic device applications.

Комбинационная способность новых дигаплодных линий кукурузы в условиях Правобережья Саратовской области

Dihaploid lines, or lines obtained on the basis of doubled haploids, are a valuable material for the creation of high-heterosis corn hybrids. The most effective method of evaluating the starting material when selecting parental couples for obtaining hybrids is diallell analysis. The article presents the results of the analysis of indicators of 6 dihaploid corn lines and 30 simple corn hybrids of the dialle scheme. The combinative ability of lines along the stem length, the cob attachment height, and the panicle length was studied. The effects of the GCA and SCA dispersion, as well as the components of genetic dispersion for the above characteristics were revealed. Lines with high GCA and SCA rates were identified. The variation of the indicators of combinative ability during the years of the study indicates that the degree of strength of the effects of GCA and the dispersion of SCA morphometric parameters in certain lines varies to some extent under the influence of growing conditions. The analysis of the components of genetic dispersion showed that, depending on the growing conditions, the formation of stem length was influenced by 1 to 3, cob height by 1 to 3, and panicle length by 1 to 2 genes or groups of genes. A pattern has been revealed: in more favorable conditions in 2021 and 2023, the number of genes that control the manifestation of the stem length and the panicle length traits increases. A significant impact of dominance components (H1, H2) has been established. Dominance in stem length, ear height and panicle length is directed towards the parental forms with greater expression of the trait.

Upconversion Luminescence Based Direct Hybridization Assay to Detect Subfemtomolar miR‐20 a DNA Analogue in Plasma

AbstractMicroRNAs (miRNAs) are promising biomarkers especially for early‐stage cancer diagnostics, but the implementation of miRNA‐based diagnostic tests is still hindered by the limitations of current analytical methods. The small size, low concentrations in biofluids and high sequence homology of miRNAs are challenges for assay development. Currently, most of the sensitive detection methods rely on enzymatic amplification steps, which complicate the analysis and can lead to biases in quantitation. Therefore, there is an increasing need to develop enzyme‐free detection methods that are sensitive, specific and user‐friendly. In this study, a simple direct hybridization assay for the DNA analogue of miR‐20a was developed. The assay is based on upconverting nanoparticle labels, which enable ultrasensitive detection, and hairpin structured probes, which provide additional hybridization stability due to base stacking. The limit of detection was 0.73 fM with plasma recoveries between 76 % and 111 %, demonstrating that the assay could be used for direct detection of miRNAs from complex sample matrices without isolation of RNA. Due to the simplicity and the excellent sensitivity for an amplification‐free method, the assay has a great potential for miRNA‐based clinical applications.

Preventing columnar grains growth during hybrid wire arc additive manufacturing of austenitic stainless steel 316L

AbstractWire arc additive manufacturing (WAAM) is an efficient technique for producing medium to large‐size components, due to its accessibility and sustainability in fabricating large‐scale parts with high deposition rates, employing low‐cost and simple equipment, and achieving high material efficiency. Consequently, WAAM has garnered attention across various industrial sectors and experienced significant growth, particularly over the last decade, as it addresses and mitigates challenges within production markets. One of the primary limitations of WAAM is its thermal history during the process, which directly influences grain formation and microstructure heterogeneity in the resulting part. Understanding the thermal cycle of the WAAM process is thus crucial for process improvement. Typically, fabricating a part using WAAM results in a microstructure with three distinct zones along the build direction: an upper zone (thin surface layer) with fine grains, a middle zone dominated by undesirably long and large columnar grains covering more than 90% of the produced part, and a lower zone with smaller to intermediate columnar grains closer to the substrate material. These zones arise from variations in cooling rates, with the middle zone exhibiting the lowest cooling rate due to 2D conduction heat transfer. Consequently, producing a component with a microstructure comprising three different zones, with a high fraction of large and long columnar grains, significantly impacts the final mechanical properties. Therefore, controlling the size and formation of these grain zones plays a key role in improving WAAM. The aim of this work is to investigate the formation of undesired columnar grains in austenitic stainless steel 316L during WAAM and propose a simple hybrid technique by combining WAAM with a hot forging process (with or without interlayer cooling time). This approach targets the disruption of the solidification pattern of columnar grain growth during deposition progression and aims to enhance the microstructure of WAAM components.

Genomic prediction for sugarcane diseases including hybrid Bayesian-machine learning approaches.

Sugarcane smut and Pachymetra root rots are two serious diseases of sugarcane, with susceptible infected crops losing over 30% of yield. A heritable component to both diseases has been demonstrated, suggesting selection could improve disease resistance. Genomic selection could accelerate gains even further, enabling early selection of resistant seedlings for breeding and clonal propagation. In this study we evaluated four types of algorithms for genomic predictions of clonal performance for disease resistance. These algorithms were: Genomic best linear unbiased prediction (GBLUP), including extensions to model dominance and epistasis, Bayesian methods including BayesC and BayesR, Machine learning methods including random forest, multilayer perceptron (MLP), modified convolutional neural network (CNN) and attention networks designed to capture epistasis across the genome-wide markers. Simple hybrid methods, that first used BayesR/GWAS to identify a subset of 1000 markers with moderate to large marginal additive effects, then used attention networks to derive predictions from these effects and their interactions, were also developed and evaluated. The hypothesis for this approach was that using a subset of markers more likely to have an effect would enable better estimation of interaction effects than when there were an extremely large number of possible interactions, especially with our limited data set size. To evaluate the methods, we applied both random five-fold cross-validation and a structured PCA based cross-validation that separated 4702 sugarcane clones (that had disease phenotypes and genotyped for 26k genome wide SNP markers) by genomic relationship. The Bayesian methods (BayesR and BayesC) gave the highest accuracy of prediction, followed closely by hybrid methods with attention networks. The hybrid methods with attention networks gave the lowest variation in accuracy of prediction across validation folds (and lowest MSE), which may be a criteria worth considering in practical breeding programs. This suggests that hybrid methods incorporating the attention mechanism could be useful for genomic prediction of clonal performance, particularly where non-additive effects may be important.

A Simple Hybrid Approach for the Synthesis of Heat Exchanger Networks Involving Internal Utility Exchangers

A Simple Hybrid Approach for the Synthesis of Heat Exchanger Networks Involving Internal Utility Exchangers

Nonlinear dynamics of the reversed shear Alfvén eigenmode in burning plasmas

In a tokamak fusion reactor operated at steady state, the equilibrium magnetic field is likely to have reversed shear in the core region, as the noninductive bootstrap current profile generally peaks off-axis. The reversed shear Alfvén eigenmode (RSAE) as a unique branch of the shear Alfvén wave in this equilibrium, can exist with a broad spectrum in wavenumber and frequency, and be resonantly driven unstable by energetic particles (EP). After briefly discussing the RSAE linear properties in burning plasma condition, we review several key topics of the nonlinear dynamics for the RSAE through both wave-EP resonance and wave-wave coupling channels, and illustrate their potentially important role in reactor-scale fusion plasmas. By means of simplified hybrid MHD-kinetic simulations, the RSAEs are shown to have typically broad phase space resonance structure with both circulating and trapped EP, as results of weak/vanishing magnetic shear and relatively low frequency. Through the route of wave-EP nonlinearity, the dominant saturation mechanism is mainly due to the transported resonant EP radially decoupling with the localized RSAE mode structure, and the resultant EP transport generally has a convective feature. The saturated RSAEs also undergo various nonlinear couplings with other collective oscillations. Two typical routes as parametric decay and modulational instability are studied using nonlinear gyrokinetic theory, and applied to the scenario of spontaneous excitation by a finite amplitude pump RSAE. Multiple RSAEs could naturally couple and induce the spectral energy cascade into a low frequency Alfvénic mode, which may effectively transfer the EP energy to fuel ions via collisionless Landau damping. Moreover, zero frequency zonal field structure could be spontaneously excited by modulation of the pump RSAE envelope, and may also lead to saturation of the pump RSAE by both scattering into stable domain and local distortion of the continuum structure.

Water productivity at two sowing dates of simple white grain maize hybrids

In Mexico, maize stands out for its economic, food and cultural importance. Since ancient times, attempts have been made to increase maize grain yields with different alternatives, including hybridization. Currently, maize hybridization offers great advantages since from the first simple hybrids it is possible to evaluate important variables such as water productivity. The present work was carried out under field conditions in southern Sonora, in two sowing seasons: December 2022- May 2023 (SD 1) and April-August 2023 (SD 2). The research aimed to evaluate the effect of two sowing dates (SD) on phenology, water requirements, water productivity and grain yield. Three single white grain hybrids (L5 X L7; L1 X L2; L2 X L7) obtained from a dialelic design were evaluated. Phenophase occurrence time, irrigation time and interval, total water volume, evapotranspiration, grain yield and water productivity were the evaluated variables. The results showed that in SD 2 significantly reduced the time of occurrence of phenophases in all hybrids due to the effect of temperature. Irrigation time was significantly shorter in SD 2 compared to SD 1. Similarly, the irrigation interval decreased in SD 2 compared to SD 1 with a significant increase in the irrigation lamina. It was necessary to apply two more irrigations to achieve grain filling. Water productivity showed highly significant differences between hybrids and between sowing dates. The simple hybrid L5 X L7 showed better agronomic performance on both sowing dates, demonstrating tolerance to the increase in temperature on SD 2.

Low Postoperative Red Blood Requirement by Using a Simplified Hybrid Arch Replacement for Type A Aortic Dissection

Low Postoperative Red Blood Requirement by Using a Simplified Hybrid Arch Replacement for Type A Aortic Dissection

Enhanced interfacial bonding of AF/PEEK composite based on CNT/aramid nanofiber multiscale flexible-rigid structure

The application of aramid fiber (AF)/polyetheretherketone (PEEK) composites is currently hindered by the inert surface and poor wettability of AF, resulting in weak interfacial adhesion and poor mechanical properties. Surface coating and the introduction of nanostructures have been proven to be effective approaches to address this problem. Herein, a simple hybrid sizing agent has been developed to modify the AF surface, consisting of soluble polyimide (PI) as a compatibilizer, carboxyl-functionalized carbon nanotubes (CNT-COOH) as a rigid unit, and aramid nanofibers (ANF) as a flexible component. The synergetic effects of PI and the multiscale flexible-rigid structure (CNT-COOH/ANF) contribute to the formation of chemical and physical bonds between AF and PEEK matrix, further improving the interfacial adhesion and stress transfer efficiency. Attributed to the enhanced wettability and roughness of AF, compared with unsized AF, the flexural strength (220.97 MPa), modulus (13.26 GPa), ILSS (13.36 MPa), and storage modulus (12.93 GPa) of the AF/PEEK composite increase by 132.60 %, 99.00 %, 18.97 %, and 82.70 % respectively. Additionally, the flexible-rigid nanonetwork facilitates the penetration of the PEEK resin into pore spaces. This simple and effective approach exhibits promising potential in enhancing the interfacial bonding of AF/PEEK composites.

The performance of Climate Information Service in delivering scientific, local, and hybrid weather forecasts: A study case in Bangladesh

Access to reliable and skillful Climate Information Service (CIS) is crucial for smallholder farmers in Bangladesh to mitigate the impacts of rainfall variability and extremes. This study aims to systematically evaluate the performance of CIS in providing Scientific Forecast (SF) and Local Forecast (LF) to smallholders in Bangladesh. The results were then compared with farmers’ perceptions of the forecast accuracy. Additionally, the skill of a simple hybrid forecast (HF), which is an integrated system of SF and LF, was assessed using the ERA5 and ground observation datasets as benchmarks. The SF and LF data were obtained from the meteoblue hindcast and from the interview, respectively. The results indicate that, overall, LF exhibits slightly higher skill compared to SF when evaluated against the ERA5 dataset. The forecast performance, however, declines by almost half when the ground-based observations are used, associated with high false alarms. Farmers, on the other hand, perceived SF to possess superior performance compared to LF. This study demonstrates that combining the SF and LF into a simple HF yields higher forecast skill than either individual forecast, highlighting the importance of HF to deliver a reliable and trustworthy weather forecast.

MedLingua: A conceptual framework for a multilingual medical conversational agent

This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.

Multifunctional Siloxene-Decorated Laser-Inscribed Graphene Patch for Sweat Ion Analysis and Electrocardiogram Monitoring.

Potentiometric detection in complex biological fluids enables continuous electrolyte monitoring for personal healthcare; however, the commercialization of ion-selective electrode-based devices has been limited by the rapid loss of potential stability caused by electrode surface inactivation and biofouling. Here, we describe a simple multifunctional hybrid patch incorporating an Au nanoparticle/siloxene-based solid contact (SC) supported by a substrate made of laser-inscribed graphene on poly(dimethylsiloxane) for the noninvasive detection of sweat Na+ and K+. These SC nanocomposites prevent the formation of a water layer during ion-to-electron transfer, preserving 3 and 5 μV/h potential drift for the Na+ and K+ ion-selective electrodes, respectively, after 13 h of exposure. The lamellar structure of the siloxene sheets increases the SC area. In addition, the electroplated Au nanoparticles, which have a large surface area and excellent conductivity, further increased the electric double-layer capacitance at the interface between the ion-selective membranes and solid-state contacts, thus facilitating ion-to-electron transduction and ultimately improving the detection stability of Na+ and K+. Furthermore, the integrated temperature and electrocardiogram sensors in the flexible patch assist in monitoring body temperature and electrocardiogram signals, respectively. Featuring both electrochemical ion-selective and physical sensors, this patch offers immense potential for the self-monitoring of health.

Fabrication and characterization of a Bi2O3-modified chitosan@ZIF-8 nanocomposite for enhanced drug loading-releasing efficacy

In this study, a simple novel hybrid mesoporous nanomaterial derived from a metal-organic framework (ZIF-8) and chitosan, which were coated on green bismuth oxide, has been successfully synthesized, characterized, and applied to investigate its dapsone loading-releasing capability in the aqueous media. This suggested nanocomposite showed promise for drug loading from water b using hydrogen bonds, pi-pi, and electrostatic interactions. Structural and morphological analyses were performed on the proposed green synthesized nanocomposite through scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy, Brunauer–Emmett–Teller analysis, and thermogravimetric analysis. Various influencing parameters, including pH, nanocomposite dose, and contact time, were investigated to optimize the dapsone loading process. Utilizing the non-linear optimization methodology, the results show that dapsone-loading efficiency was >85 % for 50 mg.L−1 of dapsone drug. The optimum parameters for achieving maximal loading of dapsone drug were pH = 6.8, hybrid mesosphere dose = 2.6 mg.mL−1, and time = 53 min. Based on the release investigations, the dapsone-loaded nanocomposite was put into phosphate buffer saline, at pH = 7.4 and T = 37 °C, with a maximum efficiency of 93.9 after 24 h.

A simple agent-based hybrid model to simulate the biophysics of glioblastoma multiforme cells and the concomitant evolution of the oxygen field

Background and objectives: Glioblastoma multiforme (GBM) is one of the most aggressive cancers of the central nervous system. It is characterized by a high mitotic activity and an infiltrative ability of the glioma cells, neovascularization and necrosis. GBM evolution entails the continuous interplay between heterogeneous cell populations, chemotaxis, and physical cues through different scales. In this work, an agent-based hybrid model is proposed to simulate the coupling of the multiscale biological events involved in the GBM invasion, specifically the individual and collective migration of GBM cells and the concurrent evolution of the oxygen field and phenotypic plasticity. An asset of the formulation is that it is conceptually and computationally simple but allows to reproduce the complexity and the progression of the GBM micro-environment at cell and tissue scales simultaneously. Methods: The migration is reproduced as the result of the interaction between every single cell and its micro-environment. The behavior of each individual cell is formulated through genotypic variables whereas the cell micro-environment is modeled in terms of the oxygen concentration and the cell density surrounding each cell. The collective behavior is formulated at a cellular scale through a flocking model. The phenotypic plasticity of the cells is induced by the micro-environment conditions, considering five phenotypes. Results: The model has been contrasted by benchmark problems and experimental tests showing the ability to reproduce different scenarios of glioma cell migration. In all cases, the individual and collective cell migration and the coupled evolution of both the oxygen field and phenotypic plasticity have been properly simulated. This simple formulation allows to mimic the formation of relevant hallmarks of glioblastoma multiforme, such as the necrotic cores, and to reproduce experimental evidences related to the mitotic activity in pseudopalisades. Conclusions: In the collective migration, the survival of the clusters prevails at the expense of cell mitosis, regardless of the size of the groups, which delays the formation of necrotic foci and reduces the rate of oxygen consumption.

An efficient nonlinear hybrid model of energy dissipation piers utilizing fiber beam and self-developed hysteretic spring elements

Energy dissipation piers comprised of concrete filled steel tubular (CFST) columns and steel shear links can exhibit good seismic performance by reasonable collaborative optimization design of columns and links. Considering the time and economic costs of experiments and fine element analysis (FEA), an efficient and accurate numerical model is critical to investigate the hysteretic behavior of energy dissipation piers under earthquake action. Therefore, a hysteretic constitutive law for steel shear links is first proposed including the theoretical shear force-deformation skeleton curve and the unloading and reloading rules during hysteretic loading. Next, a hysteretic spring model is developed for efficiently simulating the hysteretic mechanical behavior of steel shear links, which is validated to have good accuracy with comparison to fine FEA and experimental results. Then, a simplified hybrid model consisting of the self-developed spring model and fiber beam model and two multi-scale FEA models with different levels of refinement are established for nonlinear analysis of energy dissipation piers, respectively. The comparison of numerical and test results confirms that the innovative fiber beam-spring hybrid model can accurately describe the hysteretic performance of energy dissipation piers, especially the detailed mechanical behavior of steel shear links and column feet, which provides an efficient numerical tool for analysing the collaborative working mechanism of energy dissipation piers and similar energy dissipation structures.

A simple and cost-effective hybrid stationary phase for the separation of peptides, proteins and benzene derivatives through liquid chromatography.

A very simple and cost-effective silica-based hybrid stationary phase was synthesized for the separation of five synthetic peptides, five proteins, and benzene derivatives. Silica monolith was synthesized via sol-gel process. Particles obtained through the grinding of silica monolith were suspended in methanol and sedimented under gravity to obtain sub-2 μm particles. A hybrid LC stationary phase was obtained by coating an ethylene glycol dimethacrylate co-polymer onto the particles via RAFT polymerization. The resultant stationary phase was characterized using SEM, FTIR, BET, and EDX analysis. A stainless steel column (200 mm long × 0.2 mm ID) was packed with the resultant stationary phase via pressure tapering protocol using a slurry packing machine. The column resulted in the average number of theoretical plates/meter (287 500 for synthetic peptides, 276 800 for proteins, and 226 100 for benzene derivatives) under the elution conditions of acetonitrile/50 mM ammonium format (68/32 v/v%) with a flow rate of 0.785 μL min-1. Since the column resulted in the separation of synthetic peptides, proteins, and benzene derivatives with very good chromatographic performance, it could possibly separate any complex mixture of peptides and proteins with better chromatographic performance.

A paper-in-polymer-pond (PiPP) hybrid microfluidic microplate for multiplexed ultrasensitive detection of cancer biomarkers.

Conventional affinity-based colorimetric enzyme-linked immunosorbent assay (ELISA) is one of the most widely used methods for the detection of biomarkers. However, rapid point-of-care (POC) detection of multiple cancer biomarkers by conventional ELISA is limited by long incubation time, large reagent volume, and costly instrumentation along with low sensitivity due to the nature of colorimetric methods. Herein, we have developed a reusable and cost-effective paper-in-polymer-pond (PiPP) hybrid microfluidic microplate for ultrasensitive and high-throughput multiplexed detection of disease biomarkers within an hour without using specialized instruments. A piece of pre-patterned chromatography paper placed in the PMMA polymer pond facilitates rapid protein immobilization to avoid intricate surface modifications of polymer and can be changed with a fresh paper layer to reuse the device. Reagents can be simply delivered from the top PMMA layer to multiple microwells in the middle PMMA layer via flow-through microwells, thereby increasing the efficiency of washing and avoiding repeated manual pipetting or costly robots. Quantitative colorimetric analysis was achieved by calculating the brightness of images scanned by an office scanner or a smartphone camera. Sandwich-type immunoassay was performed in the PiPP hybrid device after the optimization of multiple assay conditions. Limits of detection of 0.32 ng mL-1 for carcinoembryonic antigen (CEA) and 0.20 ng mL-1 for prostate-specific antigen (PSA) were obtained, which were about 10-fold better than those of commercial ELISA kits. We envisage that this simple but versatile hybrid device can have broad applications in various bioassays in resource-limited settings.