A Future with Ubiquitous Sensing and Intelligent Systems

SPEED: an integrated, smartphone-operated, handheld digital PCR Device for point-of-care testing

This study elaborates on the design, fabrication, and data analysis details of SPEED, a recently proposed smartphone-based digital polymerase chain reaction (dPCR) device. The dPCR chips incorporate partition diameters ranging from 50 μm to 5 μm, and these partitions are organized into six distinct blocks to facilitate image processing. Due to the superior thermal conductivity of Si and its potential for mass production, the dPCR chips were fabricated on a Si substrate. A temperature control system based on a high-power density Peltier element and a preheating/cooling PCR protocol user interface shortening the thermal cycle time. The optical design employs four 470 nm light-emitting diodes as light sources, with filters and mirrors effectively managing the light emitted during PCR. An algorithm is utilized for image processing and illumination nonuniformity correction including conversion to a monochromatic format, partition identification, skew correction, and the generation of an image correction mask. We validated the device using a range of deoxyribonucleic acid targets, demonstrating its potential applicability across multiple fields. Therefore, we provide guidance and verification of the design and testing of the recently proposed SPEED device.

Electrical properties of disordered films of van der Waals semiconductor WS2 on paper

One of the primary objectives in contemporary electronics is to develop sensors that are not only scalable and cost-effective but also environmentally sustainable. To achieve this goal, numerous experiments have focused on incorporating nanomaterial-based films, which utilize nanoparticles or van der Waals materials, on paper substrates. In this article, we present a novel fabrication technique for producing dry-abraded van der Waals films on paper, demonstrating outstanding electrical characteristics. We assess the quality and uniformity of these films by conducting a spatial resistivity characterization on a 5 × 5 cm2 dry-abraded WS2 film with an average thickness of 25 μm. Employing transfer length measurements with varying channel length-to-width ratios, we extract critical parameters, including sheet resistance and contact resistance. Notably, our findings reveal a resistivity approximately one order of magnitude lower than previous reports. The film's inherent disorder manifests as an asymmetric distribution of resistance values for specific geometries. We explore how this behavior can be effectively modeled through a random resistance network (RRN), which can reproduce the experimentally observed resistance distribution. Finally, we investigate the response of these devices under applied uniaxial strain and apply the RRN model to gain a deeper understanding of this process.

Exploring the fluorescence properties of tellurium-containing molecules and their advanced applications

This review article explores the fascinating realm of fluorescence using organochalcogen molecules, with a particular emphasis on tellurium (Te). The discussion encompasses the underlying mechanisms, structural motifs influencing fluorescence, and the applications of these intriguing phenomena. This review not only elucidates the current state of knowledge but also identifies avenues for future research, thereby serving as a valuable resource for researchers and enthusiasts in the field of fluorescence chemistry with a focus on Te-based molecules. By highlighting challenges and prospects, this review sparks a conversation on the transformative potential of Te-containing compounds across different fields, ranging from environmental solutions to healthcare and materials science applications. This review aims to provide a comprehensive understanding of the distinct fluorescence behaviors exhibited by Te-containing compounds, contributing valuable insights to the evolving landscape of chalcogen-based fluorescence research.

Artificial Intelligence Agents for Materials Sciences

The artificial intelligence (AI) tools based on large-language models may serve as a demonstration that we are reaching a groundbreaking new paradigm in which machines themselves will generate knowledge autonomously. This statement is based on the assumption that the ability to master natural languages is the ultimate frontier for this new paradigm and perhaps an essential step to achieving the so-called general artificial intelligence. Autonomous knowledge generation implies that a machine will be able, for instance, to retrieve and understand the contents of the scientific literature and provide interpretations for existing data, allowing it to propose and address new scientific problems. While one may assume that the continued development of AI tools exploiting large-language models, with more data used for training, may lead these systems to learn autonomously, this learning can be accelerated by devising human-assisted strategies to deal with specific tasks. For example, strategies may be implemented for AI tools to emulate the analysis of multivariate data by human experts or in identifying and explaining patterns in temporal series. In addition to generic AI tools, such as Chat AIs, one may conceive personal AI agents, potentially working together, that are likely to serve end users in the near future. In this perspective paper, we discuss the development of this type of agent, focusing on its architecture and requirements. As a proof-of-concept, we exemplify how such an AI agent could work to assist researchers in materials sciences.

Performance of Oral Cavity Sensors: A Systematic Review

Technological advancements are enabling new applications within biomedical engineering. As a connection point between the outer environment and the human system, the oral cavity offers unique opportunities for sensing technologies. This paper systematically reviews the performance of measurement systems tested in the human oral cavity. Performance was defined by metrics related to accuracy and agreement estimation. A comprehensive search identifying human studies that reported on the accuracy or agreement of intraoral sensors found 85 research papers. Most of the literature (62%) was in dentistry, followed by neurology (21%), and physical medicine and rehabilitation (12%). The remaining papers were on internal medicine, obstetrics, and aerospace medicine. Most of the studies applied force or pressure sensors (32%), while optical and image sensors were applied most widely across fields. The main challenges for future adoption include the lack of large human trials, the maturity of emerging technologies (e.g., biochemical sensors), and the absence of standardization of evaluation in specific fields. New research should aim to employ robust performance metrics to evaluate their systems and incorporate real-world evidence as part of the evaluation process. Oral cavity sensors offer the potential for applications in healthcare and wellbeing, but for many technologies, more research is needed.

Digital Innovation Enabled Nanomaterial Manufacturing; Machine Learning Strategies and Green Perspectives

Machine learning has been an emerging scientific field serving the modern multidisciplinary needs in the Materials Science and Manufacturing sector. The taxonomy and mapping of nanomaterial properties based on data analytics is going to ensure safe and green manufacturing with consciousness raised on effective resource management. The utilization of predictive modelling tools empowered with artificial intelligence (AI) has proposed novel paths in materials discovery and optimization, while it can further stimulate the cutting-edge and data-driven design of a tailored behavioral profile of nanomaterials to serve the special needs of application environments. The previous knowledge of the physics and mathematical representation of material behaviors, as well as the utilization of already generated testing data, received specific attention by scientists. However, the exploration of available information is not always manageable, and machine intelligence can efficiently (computational resources, time) meet this challenge via high-throughput multidimensional search exploration capabilities. Moreover, the modelling of bio-chemical interactions with the environment and living organisms has been demonstrated to connect chemical structure with acute or tolerable effects upon exposure. Thus, in this review, a summary of recent computational developments is provided with the aim to cover excelling research and present challenges towards unbiased, decentralized, and data-driven decision-making, in relation to increased impact in the field of advanced nanomaterials manufacturing and nanoinformatics, and to indicate the steps required to realize rapid, safe, and circular-by-design nanomaterials.

Design and Implementation of an HCPS-Based PCB Smart Factory System for Next-Generation Intelligent Manufacturing

The next-generation intelligent smart factory system that is proposed in this paper could improve product quality and realize flexible, efficient, and sustainable product manufacturing by comprehensively improving production and management innovation via its digital network and intelligent methods that reflect the characteristics of its printed circuit board (PCB) manufacturing design and on-site implementation. Intelligent manufacturing systems are complex systems that are composed of humans, cyber systems, and physical systems and aim to achieve specific manufacturing goals at an optimized level. Advanced manufacturing technology and next-generation artificial intelligence (AI) are deeply integrated into next-generation intelligent manufacturing (NGIM). Currently, the majority of PCB manufacturers are firms that specialize in processing orders from leading semiconductor and related product manufacturers, such as Samsung Electronics, TSMC, Samsung Electro-Mechanics, and LG Electronics. These top companies have been responsible for all product innovation, intelligent services, and system integration, with PCB manufacturers primarily playing a role in intelligent production and system integration. In this study, the main implementation areas were divided into manufacturing execution system (MES) implementation (which could operate the system using system integration), data gathering, the Industrial Internet of Things (IIoT) for production line connection, AI and real-time monitoring, and system implementation that could visualize the collected data. Finally, the prospects of the design and on-site implementation of the next-generation intelligent smart factory system that detects and controls the occurrence of quality and facility abnormalities are presented, based on the implementation system.

Materials Discovery With Machine Learning and Knowledge Discovery

Machine learning and other artificial intelligence methods are gaining increasing prominence in chemistry and materials sciences, especially for materials design and discovery, and in data analysis of results generated by sensors and biosensors. In this paper, we present a perspective on this current use of machine learning, and discuss the prospects of the future impact of extending the use of machine learning to encompass knowledge discovery as an essential step towards a new paradigm of machine-generated knowledge. The reasons why results so far have been limited are given with a discussion of the limitations of machine learning in tasks requiring interpretation. Also discussed is the need to adapt the training of students and scientists in chemistry and materials sciences, to better explore the potential of artificial intelligence capabilities.

Real-time prediction of smoking activity using machine learning based multi-class classification model

Smoking cessation efforts can be greatly influenced by providing just-in-time intervention to individuals who are trying to quit smoking. Detecting smoking activity accurately among the confounding activities of daily living (ADLs) being monitored by the wearable device is a challenging and intriguing research problem. This study aims to develop a machine learning based modeling framework to identify the smoking activity among the confounding ADLs in real-time using the streaming data from the wrist-wearable IMU (6-axis inertial measurement unit) sensor. A low-cost wrist-wearable device has been designed and developed to collect raw sensor data from subjects for the activities. A sliding window mechanism has been used to process the streaming raw sensor data and extract several time-domain, frequency-domain, and descriptive features. Hyperparameter tuning and feature selection have been done to identify best hyperparameters and features respectively. Subsequently, multi-class classification models are developed and validated using in-sample and out-of-sample testing. The developed models obtained predictive accuracy (area under receiver operating curve) up to 98.7% for predicting the smoking activity. The findings of this study will lead to a novel application of wearable devices to accurately detect smoking activity in real-time. It will further help the healthcare professionals in monitoring their patients who are smokers by providing just-in-time intervention to help them quit smoking. The application of this framework can be extended to more preventive healthcare use-cases and detection of other activities of interest.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11042-022-12349-6.

Diagnosis of COVID-19, vitality of emerging technologies and preventive measures

Coronavirus diseases-2019 (COVID-19) is becoming increasing serious and major threat to public health concerns. As a matter of fact, timely testing enhances the life-saving judgments on treatment and isolation of COVID-19 infected individuals at possible earliest stage which ultimately suppresses spread of infectious diseases. Many government and private research institutes and manufacturing companies are striving to develop reliable tests for prompt quantification of SARS-CoV-2. In this review, we summarize existing diagnostic methods as manual laboratory-based nucleic acid assays for COVID-19 and their limitations. Moreover, vitality of rapid and point of care serological tests together with emerging biosensing technologies has been discussed in details. Point of care tests with characteristics of rapidity, accurateness, portability, low cost and requiring non-specific devices possess great suitability in COVID-19 diagnosis and detection. Besides, this review also sheds light on several preventive measures to track and manage disease spread in current and future outbreaks of diseases.

Nano-engineered screen-printed electrodes: A dynamic tool for detection of viruses

There is a growing interest in the development of portable, cost-effective, and easy-to-use biosensors for the rapid detection of diseases caused by infectious viruses: COVID-19 pandemic has highlighted the central role of diagnostics in response to global outbreaks. Among all the existing technologies, screen-printed electrodes (SPEs) represent a valuable technology for the detection of various viral pathogens. During the last five years, various nanomaterials have been utilized to modify SPEs to achieve convincing effects on the analytical performances of portable SPE-based diagnostics. Herein we would like to provide the readers a comprehensive investigation about the recent combination of SPEs and various nanomaterials for detecting viral pathogens. Manufacturing methods and features advances are critically discussed in the context of early-stage detection of diseases caused by HIV-1, HBV, HCV, Zika, Dengue, and Sars-CoV-2. A detailed table is reported to easily guide readers toward the "right" choice depending on the virus of interest.

Electrochemical Biosensors for Tracing Cyanotoxins in Food and Environmental Matrices

The adoption of electrochemical principles to realize on-field analytical tools for detecting pollutants represents a great possibility for food safety and environmental applications. With respect to the existing transduction mechanisms, i.e., colorimetric, fluorescence, piezoelectric etc., electrochemical mechanisms offer the tremendous advantage of being easily miniaturized, connected with low cost (commercially available) readers and unaffected by the color/turbidity of real matrices. In particular, their versatility represents a powerful approach for detecting traces of emerging pollutants such as cyanotoxins. The combination of electrochemical platforms with nanomaterials, synthetic receptors and microfabrication makes electroanalysis a strong starting point towards decentralized monitoring of toxins in diverse matrices. This review gives an overview of the electrochemical biosensors that have been developed to detect four common cyanotoxins, namely microcystin-LR, anatoxin-a, saxitoxin and cylindrospermopsin. The manuscript provides the readers a quick guide to understand the main electrochemical platforms that have been realized so far, and the presence of a comprehensive table provides a perspective at a glance.

Imaging-based spectrometer-less optofluidic biosensors based on dielectric metasurfaces for detecting extracellular vesicles

Biosensors are indispensable tools for public, global, and personalized healthcare as they provide tests that can be used from early disease detection and treatment monitoring to preventing pandemics. We introduce single-wavelength imaging biosensors capable of reconstructing spectral shift information induced by biomarkers dynamically using an advanced data processing technique based on an optimal linear estimator. Our method achieves superior sensitivity without wavelength scanning or spectroscopy instruments. We engineered diatomic dielectric metasurfaces supporting bound states in the continuum that allows high-quality resonances with accessible near-fields by in-plane symmetry breaking. The large-area metasurface chips are configured as microarrays and integrated with microfluidics on an imaging platform for real-time detection of breast cancer extracellular vesicles encompassing exosomes. The optofluidic system has high sensing performance with nearly 70 1/RIU figure-of-merit enabling detection of on average 0.41 nanoparticle/µm2 and real-time measurements of extracellular vesicles binding from down to 204 femtomolar solutions. Our biosensors provide the robustness of spectrometric approaches while substituting complex instrumentation with a single-wavelength light source and a complementary-metal-oxide-semiconductor camera, paving the way toward miniaturized devices for point-of-care diagnostics.

Big data and machine learning for materials science

Herein, we review aspects of leading-edge research and innovation in materials science that exploit big data and machine learning (ML), two computer science concepts that combine to yield computational intelligence. ML can accelerate the solution of intricate chemical problems and even solve problems that otherwise would not be tractable. However, the potential benefits of ML come at the cost of big data production; that is, the algorithms demand large volumes of data of various natures and from different sources, from material properties to sensor data. In the survey, we propose a roadmap for future developments with emphasis on computer-aided discovery of new materials and analysis of chemical sensing compounds, both prominent research fields for ML in the context of materials science. In addition to providing an overview of recent advances, we elaborate upon the conceptual and practical limitations of big data and ML applied to materials science, outlining processes, discussing pitfalls, and reviewing cases of success and failure.

On the Challenges for the Diagnosis of SARS-CoV-2 Based on a Review of Current Methodologies

Diagnosis of COVID-19 has been challenging owing to the need for mass testing and for combining distinct types of detection to cover the different stages of the infection. In this review, we have surveyed the most used methodologies for diagnosis of COVID-19, which can be basically categorized into genetic-material detection and immunoassays. Detection of genetic material with real-time polymerase chain reaction (RT-PCR) and similar techniques has been achieved with high accuracy, but these methods are expensive and require time-consuming protocols which are not widely available, especially in less developed countries. Immunoassays for detecting a few antibodies, on the other hand, have been used for rapid, less expensive tests, but their accuracy in diagnosing infected individuals has been limited. We have therefore discussed the strengths and limitations of all of these methodologies, particularly in light of the required combination of tests owing to the long incubation periods. We identified the bottlenecks that prevented mass testing in many countries, and proposed strategies for further action, which are mostly associated with materials science and chemistry. Of special relevance are the methodologies which can be integrated into point-of-care (POC) devices and the use of artificial intelligence that do not require products from a well-developed biotech industry.

Towards smart personalized perspiration analysis: An IoT-integrated cellulose-based microfluidic wearable patch for smartphone fluorimetric multi-sensing of sweat biomarkers

Practical obstacles, such as intricate designs and expensive equipment/materials, in the fabrication of wearable sweat sensors, have limited their feasibility as a personalized healthcare device. Herein, we have fabricated a cellulose-based wearable patch, which further paired with a smartphone-based fluorescence imaging module and a self-developed smartphone app for non-invasive and in situ multi-sensing of sweat biomarkers including glucose, lactate, pH, chloride, and volume. The developed Smart Wearable Sweat Patch (SWSP) sensor comprises highly fluorescent sensing probes embedded in paper substrates, and microfluidic channels consisted of cotton threads to harvest sweat from the skin surface and to transport it to the paper-based sensing probes. The imaging module was fabricated by a 3D printer, equipped with UV-LED lamps and an optical filter to provide the in situ capability of capturing digital images of the sensors via a smartphone. A smartphone app was also designed to quantify the concentration of the biomarkers via a detection algorithm. Additionally, we have recommended an Internet of Things (IoT)-based model for our developed SWSP sensor to promote its potential application for the future. The field studies on human subjects were also conducted to investigate the feasibility of our developed SWSP sensor for the analysis of sweat biomarkers. Our findings convincingly demonstrated the applicability of our developed SWSP sensor as a smart, user-friendly, ultra-low-cost (~0.03 $ per sweat patch), portable, selective, rapid, and non-invasive healthcare monitoring device for immense applications in health personalization, sports performance monitoring, and medical diagnostics.

Molecular Features for Probing Small Amphiphilic Molecules with Self-Assembled Monolayer-Protected Nanoparticles

The sensing of small molecules poses the challenge of developing devices able to discriminate between compounds that may be structurally very similar. Here, attention has been paid to the use of self-assembled monolayer (SAM)-protected gold nanoparticles since they enable a modular approach to tune single-molecule affinity and selectivity simply by changing functional moieties (i.e., covering ligands), along with multivalent molecular recognition. To date, the discovery of monolayers suitable for a specific molecular target has relied on trial-and-error approaches, with ligand chemistry being the main criterion used to modulate selectivity and sensitivity. By using molecular dynamics, we showcase that either individual molecular characteristics and/or collective features such as ligand flexibility, monolayer organization, ligand local ordering, and interfacial solvent properties can also be exploited conveniently. The knowledge of the molecular mechanisms that drive the recognition of small molecules on SAM-covered nanoparticles will critically expand our ability to manipulate and control such supramolecular systems.

O. Microfluidic Point-of-Care Devices: New Trends and Future Prospects for eHealth Diagnostics

Point-of-care (PoC) diagnostics is promising for early detection of a number of diseases, including cancer, diabetes, and cardiovascular diseases, in addition to serving for monitoring health conditions. To be efficient and cost-effective, portable PoC devices are made with microfluidic technologies, with which laboratory analysis can be made with small-volume samples. Recent years have witnessed considerable progress in this area with “epidermal electronics”, including miniaturized wearable diagnosis devices. These wearable devices allow for continuous real-time transmission of biological data to the Internet for further processing and transformation into clinical knowledge. Other approaches include bluetooth and WiFi technology for data transmission from portable (non-wearable) diagnosis devices to cellphones or computers, and then to the Internet for communication with centralized healthcare structures. There are, however, considerable challenges to be faced before PoC devices become routine in the clinical practice. For instance, the implementation of this technology requires integration of detection components with other fluid regulatory elements at the microscale, where fluid-flow properties become increasingly controlled by viscous forces rather than inertial forces. Another challenge is to develop new materials for environmentally friendly, cheap, and portable microfluidic devices. In this review paper, we first revisit the progress made in the last few years and discuss trends and strategies for the fabrication of microfluidic devices. Then, we discuss the challenges in lab-on-a-chip biosensing devices, including colorimetric sensors coupled to smartphones, plasmonic sensors, and electronic tongues. The latter ones use statistical and big data analysis for proper classification. The increasing use of big data and artificial intelligence methods is then commented upon in the context of wearable and handled biosensing platforms for the Internet of things and futuristic healthcare systems.

Electronic Tongue Coupled to an Electrochemical Flow Reactor for Emerging Organic Contaminants Real Time Monitoring

Triclosan, which is a bacteriostatic used in household items, has raised health concerns, because it might lead to antimicrobial resistance and endocrine disorders in organisms. The detection, identification, and monitoring of triclosan and its by-products (methyl triclosan, 2,4-Dichlorophenol and 2,4,6-Trichlorophenol) are a growing need in order to update current water treatments and enable the continuous supervision of the contamination plume. This work presents a customized electronic tongue prototype coupled to an electrochemical flow reactor, which aims to access the monitoring of triclosan and its derivative by-products in a real secondary effluent. An electronic tongue device, based on impedance measurements and polyethylenimine/poly(sodium 4-styrenesulfonate) layer-by-layer and TiO₂, ZnO and TiO₂/ZnO sputtering thin films, was developed and tested to track analyte degradation and allow for analyte detection and semi-quantification. A degradation pathway trend was observable by means of principal component analysis, being the sample separation, according to sampling time, explained by 77% the total variance in the first two components. A semi-quantitative electronic tongue was attained for triclosan and methyl-triclosan. For 2,4-Dichlorophenol and 2,4,6-Trichlorophenol, the best results were achieved with only a single sensor. Finally, working as multi-analyte quantification devices, the electronic tongues could provide information regarding the degradation kinetic and concentrations ranges in a dynamic removal treatment.

Optical Interrogation Techniques for Nanophotonic Biochemical Sensors

The manipulation of light via nanoengineered surfaces has excited the optical community in the past few decades. Among the many applications enabled by nanophotonic devices, sensing has stood out due to their capability of identifying miniscule refractive index changes. In particular, when free-space propagating light effectively couples into subwavelength volumes created by nanostructures, the strongly-localized near-fields can enhance light’s interaction with matter at the nanoscale. As a result, nanophotonic sensors can non-destructively detect chemical species in real-time without the need of exogenous labels. The impact of such nanophotonic devices on biochemical sensor development became evident as the ever-growing research efforts in the field started addressing many critical needs in biomedical sciences, such as low-cost analytical platforms, simple quantitative bioassays, time-resolved sensing, rapid and multiplexed detection, single-molecule analytics, among others. In this review, the optical transduction methods used to interrogate optical resonances of nanophotonic sensors will be highlighted. Specifically, the optical methodologies used thus far will be evaluated based on their capability of addressing key requirements of the future sensor technologies, including miniaturization, multiplexing, spatial and temporal resolution, cost and sensitivity.

Heavy Metal/Toxins Detection Using Electronic Tongues

The growing concern for sustainability and environmental preservation has increased the demand for reliable, fast response, and low-cost devices to monitor the existence of heavy metals and toxins in water resources. An electronic tongue (e-tongue) is a multisensory array mostly based on electroanalytical methods and multivariate statistical techniques to facilitate information visualization in a qualitative and/or quantitative way. E-tongues are promising analytical devices having simple operation, fast response, low cost, easy integration with other systems (microfluidic, optical, etc) to enable miniaturization and provide a high sensitivity for measurements in complex liquid media, providing an interesting alternative to address many of the existing environmental monitoring challenges, specifically relevant emerging pollutants such as heavy metals and toxins.

Graphene-Based Sensors for Human Health Monitoring

Since the desire for real-time human health monitoring as well as seamless human-machine interaction is increasing rapidly, plenty of research efforts have been made to investigate wearable sensors and implantable devices in recent years. As a novel 2D material, graphene has aroused a boom in the field of sensor research around the world due to its advantages in mechanical, thermal, and electrical properties. Numerous graphene-based sensors used for human health monitoring have been reported, including wearable sensors, as well as implantable devices, which can realize the real-time measurement of body temperature, heart rate, pulse oxygenation, respiration rate, blood pressure, blood glucose, electrocardiogram signal, electromyogram signal, and electroencephalograph signal, etc. Herein, as a review of the latest graphene-based sensors for health monitoring, their novel structures, sensing mechanisms, technological innovations, components for sensor systems and potential challenges will be discussed and outlined.

Use of data processing for rapid detection of the prostate-specific antigen biomarker using immunomagnetic sandwich-type sensors

Diagnosis of cancer using electroanalytical methods can be achieved at low cost and in rapid assays, but this may require the combination with data treatment for determining biomarkers in real samples. In this paper, we report an immunomagnetic nanoparticle-based microfluidic sensor (INμ-SPCE) for the amperometric detection of the prostate-specific antigen (PSA) biomarker, the data of which were treated with information visualization methods. The INμ-SPCE consists of eight working electrodes, reference and counter electrodes. On the working electrodes, magnetic nanoparticles with secondary antibodies with the enzyme horseradish peroxidase were immobilized for the indirect detection of PSA in a sandwich-type procedure. Under optimal conditions, the immunosensor could operate within a wide range from 12.5 to 1111 fg·L^-1, with a low detection limit of 0.062 fg·L^-1. Multidimensional projections combined with feature selection allowed for the distinction of cell lysates with different levels of PSA, in agreement with results from the traditional enzyme-linked immunosorbent assay. The approaches for immunoassays and data processing are generic, and therefore the strategies described here may provide a simple platform for clinical diagnosis of cancers and other types of diseases.