Detection and monitoring of lactate and glucose levels in biological fluids and cell cultures are essential for understanding metabolic disorders. While electrochemical biosensors are commonly used, traditional enzymatic sensors face challenges related to stability, reproducibility, and cost. To address these limitations, we developed non-enzymatic sensors for lactate and glucose detection using nanostructured nickel oxide (NiO)–modified screen-printed carbon electrodes. The sensors were fabricated by drop-casting a NiO/Nafion/ethanol dispersion onto the working electrode, and their performance was evaluated using cyclic voltammetry and amperometry. Optimal sensitivity and linearity were achieved at a working potential of ∼0.5 V. The sensors exhibited linear responses for both lactate and glucose in the 0.1–5 mM range, with detection limits of 0.03 mM (lactate) and 0.025 mM (glucose), and sensitivities of 1.564 μA/mM (lactate) and 1.842 μA/mM (glucose) in 0.1 M NaOH–KCl electrolyte. To address glucose interference in lactate sensing, dual-sensing strategies were employed by varying Nafion concentration, applying differential potentials, or modifying the sensors with Prussian Blue to achieve selective detection. Validation against commercial lactate and glucose assay kits in cell culture medium showed good agreement, confirming the sensors’ accuracy. Finally, the sensor was integrated with a microfluidic chip, demonstrating its potential as a flow-through, enzyme-free metabolic sensor for future organ-on-a-chip applications.

Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays.

The health hazards of micro- and nanoplastic contaminants in drinking water has recently emerged as an area of concern to policy makers and industry. Plastic contaminants range in size from micro- (5 mm to 1 μm) to nanoplastics (<1 μm). Microfluidics provides many tools for particle manipulation at the microscale, particularly in diagnostics and biomedicine, but has in general a limited capacity to process large volumes. Drinking water and environmental samples with low-level contamination of microplastics require processing of deciliter to liter sample volumes to achieve statistically relevant particle counts. Here, we introduce the EchoGrid, an acoustofluidics device for high throughput continuous flow particle enrichment into a robust array of particle clusters. The EchoGrid takes advantage of highly efficient particle capture through the integration of a micropatterned transducer for surface displacement-based acoustic trapping in a glass and polymer microchannel. Silica seed particles were used as anchor particles to improve capture performance at low particle concentrations and high flow rates. The device was able to maintain the silica grids at a flow rate of 50 mL/min. In terms of enrichment, the device is able to double the final pellet’s microplastic concentration every 78 s for 23 μm particles and every 51 s for 10 μm particles at a flow rate of 5 mL/min. In conclusion, we demonstrate the usefulness of the EchoGrid by capturing microplastics in challenging conditions, such as large sample volumes with low microparticle concentrations, without sacrificing the potential of integration with downstream analysis for environmental monitoring.

Micro- and nanoplastics have become increasingly relevant as contaminants to be monitored due to their potential health effects and environmental impact. Nanoplastics, in particular, have been shown to be difficult to detect in drinking water, requiring new capture technologies. In this work, we applied the acoustofluidic seed particle method to capture nanoplastics in an optimized, tilted grid of silica clusters even at the high flow rate of 5 mL/min. Moreover, we achieved, using this technique, the enrichment of nanoparticles ranging from 500 nm to 25 nm as a first in the field. We employed fluorescence to observe the enrichment profiles according to size, using a washing buffer flow at 0.5 mL/min, highlighting the size-dependent nature of the silica seed particle release of various sizes of nanoparticles. These results highlight the versatility of acoustic trapping for a wide range of nanoplastic particles and allow further study into the complex dynamics of the seed particle method at these size ranges. Moreover, with reproducible size-dependent washing curves, we provide a new window into the rate of nanoplastic escape in high-capacity acoustic traps, relevant to both environmental and biomedical applications.

3D cell culture models are important tools in translational research but have been out of reach for high-throughput screening due to complexity, requirement of large cell numbers and inadequate standardization. Microfluidics and culture model miniaturization technologies could overcome these challenges. Here, we present a high throughput workflow to produce and characterize the formation of miniaturized spheroids using deep learning. We train a convolutional neural network (CNN) for cell ensemble morphology classification for droplet microfluidic minispheroid production, benchmark it against more conventional image analysis, and characterize minispheroid assembly determining optimal surfactant concentrations and incubation times for minispheroid production for three cell lines with different spheroid formation properties. Notably, this format is compatible with large-scale spheroid production and screening. The presented workflow and CNN offer a template for large scale minispheroid production and analysis and can be extended and re-trained to characterize morphological responses in spheroids to additives, culture conditions and large drug libraries.

Microfluidics has emerged rapidly over the past 20 years and has been investigated for a variety of applications from life sciences to environmental monitoring. Although continuous-flow microfluidics is ubiquitous, segmented-flow or droplet microfluidics offers several attractive features. Droplets can be independently manipulated and analyzed with very high throughput. Typically, microfluidics is carried out within planar networks of microchannels, namely, microfluidic chips. We propose that fibers offer an interesting alternative format with key advantages for enhanced optical coupling. Herein, we demonstrate the generation of monodisperse droplets within a uniaxial optofluidic Lab-in-a-Fiber scheme. We combine droplet microfluidics with laser-induced fluorescence (LIF) detection achieved through the development of an optical side-coupling fiber, which we term a periscope fiber. This arrangement provides stable and compact alignment. Laser-induced fluorescence offers high sensitivity and low detection limits with a rapid response time making it an attractive detection method for in situ real-time measurements. We use the well-established fluorophore, fluorescein, to characterize the Lab-in-a-Fiber device and determine the generation of similar to 0.9 nL droplets. We present characterization data of a range of fluorescein concentrations, establishing a limit of detection (LOD) of 10 nM fluorescein. Finally, we show that the device operates within a realistic and relevant fluorescence regime by detecting reverse-transcription loop-mediated isothermal amplification (RT-LAMP) products in the context of COVID-19 diagnostics. The device represents a step towards the development of a point- of-care droplet digital RT-LAMP platform.

Droplet microfluidics utilize a monodisperse water-in-oil emulsion, with an expanding toolbox offering a wide variety of operations on a range of droplet sizes at high throughput. However, translation of these capabilities into applications for non-expert laboratories to fully harness the inherent potential of microscale manipulations is woefully trailing behind. One major obstacle is that droplet microfluidic setups often rely on custom fabricated devices, costly liquid actuators, and are not easily set up and operated by non-specialists. This impedes wider adoption of droplet technologies in, e.g., the life sciences. Here, we demonstrate an easy-to-use minimal droplet production setup with a small footprint, built exclusively from inexpensive commercially sourced parts, powered and controlled by a laptop. We characterize the components of the system and demonstrate production of droplets ranging in volume from 3 to 21 nL in a single microfluidic device. Furthermore, we describe the dynamic tuning of droplet composition. Finally, we demonstrate the production of droplet-templated cell spheroids from primary cells, where the mobility and simplicity of the setup enables its use within a biosafety cabinet. Taken together, we believe this minimal droplet setup is ideal to drive broad adoption of droplet microfluidics technology.

Here, we build, train and apply an automated imaging and deep learning image analysis pipeline for optimization of assembly and culture conditions for miniaturized 3D cell spheroids production in microfluidic droplets. Miniaturization of spheroids, rapid assembly optimization and automated spheroid analysis would amount to a paradigm shift in early drug development. We expand an automated ultra-high-throughput workflow for minispheroid production in microfluidic droplets by training a convolutional neural network (CNN) model for automated minispheroid morphology assessment and classification. The CNN classifier was used to characterize minispheroid assembly of three different cell lines for a range of incubation times and surfactant concentrations.

Silica fibers and capillaries offer opportunities for compact integration of optics with microfluidics while adding advantages such as; flexibility within a high aspect ratio format, uniaxial arrangements, and measurement-at-a-distance. Here, we describe droplet microfluidics-based nucleic acid detection of SARS-CoV-2 in a lab-in-a-fiber platform. The fiber component integrates three modules with key functions: droplet generation, incubation, and fluorescence detection. Within the scope of this work, we developed the component specifically to target the quantification of SARS-CoV-2 viral RNA through reverse-transcription loop-mediated isothermal amplification (RT-LAMP). The all-fiber component could successfully generate uniform droplets and differentiate pre-amplified positive LAMP reaction from negative sample.