Diluting organic semiconductors with a host insulating polymer is used to increase the electronic mobility in organic electronic devices, such as thin film transistors, while considerably reducing material costs. In contrast to organic electronics, bioelectronic devices such as the organic electrochemical transistor (OECT) rely on both electronic and ionic mobility for efficient operation, making it challenging to integrate hydrophobic polymers as the predominant blend component. This work shows that diluting the n‐type conjugated polymer p(N‐T) with high molecular weight polystyrene (10 KDa) leads to OECTs with over three times better mobility‐volumetric capacitance product (µC*) with respect to the pristine p(N‐T) (from 4.3 to 13.4 F V⁻¹ cm⁻¹ s⁻¹) while drastically decreasing the amount of conjugated polymer (six times less). This improvement in µC* is due to a dramatic increase in electronic mobility by two orders of magnitude, from 0.059 to 1.3 cm² V⁻¹ s⁻¹ for p(N‐T):Polystyrene 10 KDa 1:6. Moreover, devices made with this polymer blend show better stability, retaining 77% of the initial drain current after 60 minutes operation in contrast to 12% for pristine p(N‐T). These results open a new generation of low‐cost organic mixed ionic‐electronic conductors where the bulk of the film is made by a commodity polymer.

Extracellular vesicles are small membrane-bound structures that are released by cells and play important roles in intercellular communication garnering significant attention in scientific society recently due to their potential as diagnostic and therapeutic tools. However, separating EVs from large-volume samples remains a challenge due to their small size and low concentration. In this manuscript, we presented a novel method for separating polystyrene beads as control and extracellular vesicles from large sample volumes using bifurcated asymmetric field flow fractionation in PDMS-free microfluidic devices. Separation characteristics were evaluated using the control system of polystyrene bead mix, which offers up to 3.7X enrichment of EV-sized beads. Furthermore, in the EV-sample from bioreactor culture media, we observed a notable population distribution shift of extracellular vesicles. Herein presented novel PDMS-free microfluidic device fabrication protocol resulted in devices with reduced EV-loss compared to size-exclusion columns. This method represented an improvement over the current state of the art in terms of EV separation from large sample volumes through the use of novel field flow fractionation design.

Extracellular vesicles (EV) have many attributes important for biomedicine; however, current EV isolation methods require long multi-step protocols that generally involve bulky equipment that cannot be easily translated to clinics. Our aim was to design a new cyclic olefin copolymer–off-stoichiometry thiol-ene (COC–OSTE) asymmetric flow field fractionation microfluidic device that could isolate EV from high-volume samples in a simple and efficient manner. We tested the device with large volumes of urine and conditioned cell media samples, and compared it with the two most commonly used EV isolation methods. Our device was able to separate particles by size and buoyancy, and the attained size distribution was significantly smaller than other methods. This would allow for targeting EV size fractions of interest in the future. However, the results were sample dependent, with some samples showing significant improvement over the current EV separation methods. We present a novel design for a COC–OSTE microfluidic device, based on bifurcating asymmetric flow field-flow fractionation (A4F) technology, which is able to isolate EV from large volume samples in a simple, continuous-flow manner. Its potential to be mass-manufactured increases the chances of implementing EV isolation in a clinical or industry-friendly setting, which requires high repeatability and throughput.

Current in vitro models have significant limitations for new respiratory disease research and rapid drug repurposing. Lung on a chip (LOAC) technology offers a potential solution to these problems. However, these devices typically are fabricated from polydimethylsiloxane (PDMS), which has small hydrophobic molecule absorption, which hinders the application of this technology in drug repurposing for respiratory diseases. Off-stoichiometry thiol–ene (OSTE) is a promising alternative material class to PDMS. Therefore, this study aimed to test OSTE as an alternative material for LOAC prototype development and compare it to PDMS. We tested OSTE material for light transmission, small molecule absorption, inhibition of enzymatic reactions, membrane particle, and fluorescent dye absorption. Next, we microfabricated LOAC devices from PDMS and OSTE, functionalized with human umbilical vein endothelial cell (HUVEC) and A549 cell lines, and analyzed them with immunofluorescence. We demonstrated that compared to PDMS, OSTE has similar absorption of membrane particles and effect on enzymatic reactions, significantly lower small molecule absorption, and lower light transmission. Consequently, the immunofluorescence of OSTE LOAC was significantly impaired by OSTE optical properties. In conclusion, OSTE is a promising material for LOAC, but optical issues should be addressed in future LOAC prototypes to benefit from the material properties.

The possibility to artificially adjust and fine-tune gene expression is one of the key milestones in bioengineering, synthetic biology, and advanced medicine. Since the effects of proteins or other transgene products depend on the dosage, controlled gene expression is required for any applications, where even slight fluctuations of the transgene product impact its function or other critical cell parameters. In this context, physical techniques demonstrate optimistic perspectives, and pulsed electric field technology is a potential candidate for a noninvasive, biophysical gene regulator, exploiting an easily adjustable pulse generating device. We exposed mammalian cells, transfected with a NF-κB pathway-controlled transcription system, to a range of microsecond-duration pulsed electric field parameters. To prevent toxicity, we used protocols that would generate relatively mild physical stimulation. The present study, for the first time, proves the principle that microsecond-duration pulsed electric fields can alter single-gene expression in plasmid context in mammalian cells without significant damage to cell integrity or viability. Gene expression might be upregulated or downregulated depending on the cell line and parameters applied. This noninvasive, ligand-, cofactor-, nanoparticle-free approach enables easily controlled direct electrostimulation of the construct carrying the gene of interest; the discovery may contribute towards the path of simplification of the complexity of physical systems in gene regulation and create further synergies between electronics, synthetic biology, and medicine.

Organ-on-a-chip (OOC) technology has emerged as a groundbreaking approach for emulating the physiological environment, revolutionizing biomedical research, drug development, and personalized medicine. OOC platforms offer more physiologically relevant microenvironments, enabling real-time monitoring of tissue, to develop functional tissue models. Imaging methods are the most common approach for daily monitoring of tissue development. Image-based machine learning serves as a valuable tool for enhancing and monitoring OOC models in real-time. This involves the classification of images generated through microscopy contributing to the refinement of model performance. This paper presents an image dataset, containing cell images generated from OOC setup with different cell types. There are 3072 images generated by an automated brightfield microscopy setup. For some images, parameters such as cell type, seeding density, time after seeding and flow rate are provided. These parameters along with predefined criteria can contribute to the evaluation of image quality and identification of potential artifacts. This dataset can be used as a basis for training machine learning classifiers for automated data analysis generated from an OOC setup providing more reliable tissue models, automated decision-making processes within the OOC framework and efficient research in the future.