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Expanding our global testing capacity is critical to preventing and containing pandemics1-9. Accordingly, accessible and adaptable automated platforms that in decentralized settings perform nucleic acid amplification tests resource-efficiently are required10-14. Pooled testing can be extremely efficient if the pooling strategy is based on local viral prevalence15-20; however, it requires automation, small sample volume handling and feedback not available in current bulky, capital-intensive liquid handling technologies21-29. Here we use a swarm of millimetre-sized magnets as mobile robotic agents ('ferrobots') for precise and robust handling of magnetized sample droplets and high-fidelity delivery of flexible workflows based on nucleic acid amplification tests to overcome these limitations. Within a palm-sized printed circuit board-based programmable platform, we demonstrated the myriad of laboratory-equivalent operations involved in pooled testing. These operations were guided by an introduced square matrix pooled testing algorithm to identify the samples from infected patients, while maximizing the testing efficiency. We applied this automated technology for the loop-mediated isothermal amplification and detection of the SARS-CoV-2 virus in clinical samples, in which the test results completely matched those obtained off-chip. This technology is easily manufacturable and distributable, and its adoption for viral testing could lead to a 10-300-fold reduction in reagent costs (depending on the viral prevalence) and three orders of magnitude reduction in instrumentation cost. Therefore, it is a promising solution to expand our testing capacity for pandemic preparedness and to reimagine the automated clinical laboratory of the future.

Advancements in wearable bioanalytical microsystems have enabled diurnal and (semi)continuous monitoring of physiologically-relevant indices that are accessible through probing sweat. To deliver an undistorted and physiologically-meaningful interpretation of these readings, tracking the sweat secretion rate is essential, because it allows for calibrating the biomarker readings against variations in sweat secretion and inferring the bodys hydration/electrolyte homeostasis status. To realize an autonomous wearable solution with intrinsically high signal-to-noise ratio sweat rate sensing capabilities, here, we devise a digitized microbubble detection mechanism-delivered by a hybrid microfluidic/electronic system with a compact footprint. This mechanism is based on the intermittent generation of microliter-scale bubbles via electrolysis and the instantaneous measurement of their time-of-flight (and thus, velocity) via impedimetric sensing. In this way, we overcome the limitations of previously proposed sweat rate sensing modalities that are inherently susceptible to non-targeted secretion characteristics (pH, conductivity, and temperature), constrained by volume, or lack system integration for autonomous on-body operation. By deploying our solution in human subject trials, we validate the utility of our solution for seamless monitoring of exercise- and iontophoretically-induced sweat secretion profiles.

Therapeutic drug monitoring is essential for dosing pharmaceuticals with narrow therapeutic windows. Nevertheless, standard methods are imprecise and involve invasive/resource-intensive procedures with long turnaround times. Overcoming these limitations, we present a microneedle-based electrochemical aptamer biosensing patch (μNEAB-patch) that minimally invasively probes the interstitial fluid (ISF) and renders correlated, continuous, and real-time measurements of the circulating drugs' pharmacokinetics. The μNEAB-patch is created following an introduced low-cost fabrication scheme, which transforms a shortened clinical-grade needle into a high-quality gold nanoparticle-based substrate for robust aptamer immobilization and efficient electrochemical signal retrieval. This enables the reliable in vivo detection of a wide library of ISF analytes-especially those with nonexistent natural recognition elements. Accordingly, we developed μNEABs targeting various drugs, including antibiotics with narrow therapeutic windows (tobramycin and vancomycin). Through in vivo animal studies, we demonstrated the strong correlation between the ISF/circulating drug levels and the device's potential clinical use for timely prediction of total drug exposure.

The awareness of individuals’ biological status is critical for creating interactive and adaptive environments that can actively assist the users to achieve optimal outcomes. Accordingly, specialized human–machine interfaces—equipped with bioperception and interpretation capabilities—are required. To this end, we devised a multimodal cryptographic bio-human–machine interface (CB-HMI), which seamlessly translates the user’s touch-based entries into encrypted biochemical, biophysical, and biometric indices. As its central component, the CB-HMI features thin hydrogel-coated chemical sensors and inference algorithms to noninvasively and inconspicuously acquire biochemical indices such as circulating molecules that partition onto the skin (here, ethanol and acetaminophen). Additionally, the CB-HMI hosts physical sensors and associated algorithms to simultaneously acquire the user’s heart rate, blood oxygen level, and fingerprint minutiae pattern. Supported by human subject studies, we demonstrated the CB-HMI’s capability in terms of acquiring physiologically relevant readouts of target bioindices, as well as user-identifying and biometrically encrypting/decrypting these indices in situ (leveraging the fingerprint feature). By upgrading the common surrounding objects with the CB-HMI, we created interactive solutions for driving safety and medication use. Specifically, we demonstrated a vehicle-activation system and a medication-dispensing system, where the integrated CB-HMI uniquely enabled user bioauthentication (on the basis of the user’s biological state and identity) prior to rendering the intended services. Harnessing the levels of bioperception achieved by the CB-HMI and other intelligent HMIs, we can equip our surroundings with a comprehensive and deep awareness of individuals’ psychophysiological state and needs.

Wearable technologies for personalized monitoring require sensors that track biomarkers often present at low levels. Cortisol—a key stress biomarker—is present in sweat at low nanomolar concentrations. Previous wearable sensing systems are limited to analytes in the micromolar-millimolar ranges. To overcome this and other limitations, we developed a flexible field-effect transistor (FET) biosensor array that exploits a previously unreported cortisol aptamer coupled to nanometer-thin-film In2O3 FETs. Cortisol levels were determined via molecular recognition by aptamers where binding was transduced to electrical signals on FETs. The physiological relevance of cortisol as a stress biomarker was demonstrated by tracking salivary cortisol levels in participants in a Trier Social Stress Test and establishing correlations between cortisol in diurnal saliva and sweat samples. These correlations motivated the development and on-body validation of an aptamer-FET array–based smartwatch equipped with a custom, multichannel, self-referencing, and autonomous source measurement unit enabling seamless, real-time cortisol sweat sensing.

Active biofluid management is central to the realization of wearable bioanalytical platforms that are poised to autonomously provide frequent, real-time, and accurate measures of biomarkers in epidermally-retrievable biofluids (e.g., sweat). Accordingly, here, a programmable epidermal microfluidic valving system is devised, which is capable of biofluid sampling, routing, and compartmentalization for biomarker analysis. At its core, the system is a network of individually-addressable microheater-controlled thermo-responsive hydrogel valves, augmented with a pressure regulation mechanism to accommodate pressure built-up, when interfacing sweat glands. The active biofluid control achieved by this system is harnessed to create unprecedented wearable bioanalytical capabilities at both the sensor level (decoupling the confounding influence of flow rate variability on sensor response) and the system level (facilitating context-based sensor selection/protection). Through integration with a wireless flexible printed circuit board and seamless bilateral communication with consumer electronics (e.g., smartwatch), contextually-relevant (scheduled/on-demand) on-body biomarker data acquisition/display was achieved.

To achieve the mission of personalized medicine, centering on delivering the right drug to the right patient at the right dose, therapeutic drug monitoring solutions are necessary. In that regard, wearable biosensing technologies, capable of tracking drug pharmacokinetics in noninvasively retrievable biofluids (e.g., sweat), play a critical role, because they can be deployed at a large scale to monitor the individuals' drug transcourse profiles (semi)continuously and longitudinally. To this end, voltammetry-based sensing modalities are suitable, as in principle they can detect and quantify electroactive drugs on the basis of the target's redox signature. However, the target's redox signature in complex biofluid matrices can be confounded by the immediate biofouling effects and distorted/buried by the interfering voltammetric responses of endogenous electroactive species. Here, we devise a wearable voltammetric sensor development strategy-centering on engineering the molecule-surface interactions-to simultaneously mitigate biofouling and create an "undistorted potential window" within which the target drug's voltammetric response is dominant and interference is eliminated. To inform its clinical utility, our strategy was adopted to track the temporal profile of circulating acetaminophen (a widely used analgesic and antipyretic) in saliva and sweat, using a surface-modified boron-doped diamond sensing interface (cross-validated with laboratory-based assays, R 2 ∼ 0.94). Through integration of the engineered sensing interface within a custom-developed smartwatch, and augmentation with a dedicated analytical framework (for redox peak extraction), we realized a wearable solution to seamlessly render drug readouts with minute-level temporal resolution. Leveraging this solution, we demonstrated the pharmacokinetic correlation and significance of sweat readings.

To render high-fidelity wearable biomarker data, understanding and engineering the information delivery pathway from epidermally retrieved biofluid to a readout unit are critical. By examining the biomarker information delivery pathway and recognizing near-zero strained regions within a microfluidic device, a strain-isolated pathway to preserve biomarker data fidelity is engineered. Accordingly, a generalizable and disposable freestanding electrochemical sensing system (FESS) is devised, which simultaneously facilitates sensing and out-of-plane signal interconnection with the aid of double-sided adhesion. The FESS serves as a foundation to realize a system-level design strategy, addressing the challenges of wearable biosensing, in the presence of motion, and integration with consumer electronics. To this end, a FESS-enabled smartwatch was developed, featuring sweat sampling, electrochemical sensing, and data display/transmission, all within a self-contained wearable platform. The FESS-enabled smartwatch was used to monitor the sweat metabolite profiles of individuals in sedentary and high-intensity exercise settings.

The maximum spreading factor during droplet impact on a dry surface is a pivotal parameter of a range of applications, including inkjet printing, anti-icing, and micro-droplet transportation. It is determined by a combination of the inertial force, viscous force, surface tension, and fluid–solid interaction. There are currently a series of qualitative and quantitative prediction models for the maximum spreading factor rooted in both momentum and energy conservation. However, the performance of these models on consistent experimental samples remains ambiguous. In this work, a comprehensive set of 785 experimental samples spanning the last four decades is compiled. These samples encompass Weber numbers ranging from 0.038 to 2447.7 and Reynolds numbers from 9 to 34 339. A prediction model is introduced that employs a neural network, which achieves an average relative error of less than 16.6% with a standard error of 0.018 08 when applied to the test set. Following this, a fair comparison is presented of the accuracy, generality, and stability of different prediction models. Although the neural network model provides superior accuracy and generality, its stability is weaker than that of Scheller's We-Re-dependent formula, chiefly due to the absence of physical constraints. Subsequently, a physics-informed prediction model is introduced by considering a physical loss term. This model demonstrates comprehensive enhancements compared to the original neural network, and the average relative and standard errors for this model are reduce to 13.6% and 0.010 59, respectively. This novel model should allow for the rapid and precise prediction of the maximum spreading factor across a broad range of parameters for various applications. [ABSTRACT FROM AUTHOR]