Unlike most synthetic systems, life constantly reorganizes itself through the irreversible consumption of energy-rich molecules and exhibits dynamic functionalities governed by spatiotemporally controlled biocatalytic processes. Inspired by this, we herein demonstrate unidirectionally communicating, out-of-equilibrium DNA circuits that enable network-guided control of the biocatalytic activity of an enzyme. The unidirectional communication is realized through the programmed, dissipative manipulation of information transfer. In this process, transient activation of a DNAzyme generates the fuel required for the temporal activation of trypsin. Prior to establishing this information-transfer framework, we employed fuel-driven dissipation to autonomously and temporally regulate the activity of nucleic acid and protein-based enzymes, each operating in individual cycles. The transient state of the systems is attained through rapid hybridization of DNA strands, while digestion of the DNA fuel by exonucleases regenerates the initial equilibrium state. These processes proceed in a cyclic manner, allowing the systems to attain an out-of-equilibrium state. Precise control over the lifetime of this transient state was achieved by regulating external factors, such as DNA fuel and exonuclease concentrations, and internally by exploiting the toe-hold length-dependent digestion kinetics of the exonucleases. To establish our findings, we adopted a combined approach that includes both experimental and computational methodologies.

Electrochemical reduction of CO 2 poses a vast potential to contribute to a defossilized industry. Despite tremendous developments within the field, mass transport limitations, carbonate salt formation, and electrode degradation mechanisms still hamper the process performance. One promising approach to tweak CO 2 electrolysis beyond today's limitations is pulsed electrolysis with potential cycling between an operating and a regeneration mode. Here, we rigorously model the boundary layer at a silver electrode in pulsed operation to get profound insights into the dynamic reorganization of the electrode microenvironment. In our simulation, pulsed electrolysis leads to a significant improvement of up to six times higher CO current density and 20 times higher cathodic energy efficiency when pulsing between −1.85 and −1.05 V vs SHE compared to constant potential operation. We found that elevated reactant availability in pulsed electrolysis originates from alternating replenishment of CO 2 by diffusion and not from pH-induced carbonate and bicarbonate conversion. Moreover, pulsed electrolysis substantially promotes carbonate removal from the electrode by up to 83 % compared to constant potential operation, thus reducing the risk of salt formation. Therefore, this model lays the groundwork for an accurate simulation of the dynamic boundary layer modulation, which can provide insights into manifold electrochemical conversions.

In vitro environments that realize biomimetic scaffolds, cellular composition, physiological shear, and strain are integral to developing tissue models of organ-specific functions. In this study, an in vitro pulmonary alveolar capillary barrier model is developed that closely mimics physiological functions by combining a synthetic biofunctionalized nanofibrous membrane system with a novel three-dimensional (3D)-printed bioreactor. The fiber meshes are fabricated from a mixture of polycaprolactone (PCL), 6-armed star-shaped isocyanate-terminated poly(ethylene glycol) (sPEG-NCO), and Arg-Gly-Asp (RGD) peptides by a one-step electrospinning process that offers full control over the fiber surface chemistry. The tunable meshes are mounted within the bioreactor where they support the co-cultivation of pulmonary epithelial (NCI-H441) and endothelial (HPMEC) cell monolayers at air-liquid interface under controlled stimulation by fluid shear stress and cyclic distention. This stimulation, which closely mimics blood circulation and breathing motion, is observed to impact alveolar endothelial cytoskeleton arrangement and improve epithelial tight junction formation as well as surfactant protein B production compared to static models. The results highlight the potential of PCL-sPEG-NCO:RGD nanofibrous scaffolds in combination with a 3D-printed bioreactor system as a platform to reconstruct and enhance in vitro models to bear a close resemblance to in vivo tissues.

Organic neuromorphic devices can accelerate neural networks and integrate with biological systems. Devices based on the biocompatible and conductive polymer PEDOT:PSS are fast, require low amounts of energy and perform well in crossbar simulations. However, parasitic electrochemical reactions lead to self-discharge and the fading of the learned conductance states over time. This limits a neural network’s operating time and requires complex compensation mechanisms. Spiking neural networks (SNNs) take inspiration from biology to implement local and always-on learning. We show that these SNNs can function on organic neuromorphic hardware and compensate for self-discharge by continuously relearning and reinforcing forgotten states. In this work, we use a high-resolution charge transport model to describe the behavior of organic neuromorphic devices and create a computationally efficient surrogate model. By integrating the surrogate model into a Brian 2 simulation, we can describe the behavior of SNNs on organic neuromorphic hardware. A biologically plausible two-layer network for recognizing 28 × 28 pixel MNIST images is trained and observed during self-discharge. The network achieves, for its size, competitive recognition results of up to 82.5%. Building a network with forgetful devices yields superior accuracy during training with 84.5% compared to ideal devices. However, trained networks without active spike-timing-dependent plasticity quickly lose their predictive performance. We show that online learning can keep the performance at a steady level close to the initial accuracy, even for idle rates of up to 90%. This performance is maintained when the output neuron’s labels are not revalidated for up to 24 h. These findings reconfirm the potential of organic neuromorphic devices for brain-inspired computing. Their biocompatibility and the demonstrated adaptability to SNNs open the path towards close integration with multi-electrode arrays, drug-delivery devices, and other bio-interfacing systems as either fully organic or hybrid organic-inorganic systems.

Electro-Fenton (EF) represents an eco-friendly and cost-effective advanced oxidation process that can remove highly persistent and hazardous pharmaceuticals, e.g., contrast media agents, from water bodies. However, up to date, EF modules incorporate a planar carbonaceous gas diffusion electrode (GDE) cathode containing fluorinated compounds as polymeric binders. Here, we introduce a novel flow-through module that deploys freestanding carbon microtubes (CMT) as microtubular GDEs, omitting any risks of secondary pollution by highly persistent fluorinated compounds (e.g., Nafion). The flow-through module was characterized for electrochemical hydrogen peroxide (H2O2) generation and micropollutant removal via EF. H2O2 electro-generation experiments illustrated high production rates (1.1 & PLUSMN; 0.1-2.7 & PLUSMN; 0.1 mg cm-2 h-1) at an applied cathodic potential of 0.6 V vs. SHE, depending on the porosity of CMTs. Diatrizoate (DTZ), as the model pollutant, with a high initial concentration of 100 mg L-1 was successfully oxidized (95-100 %), reaching mineralization (TOC-total organic carbon removal) efficiencies up to 69 %. Additionally, Electro-adsorption experiments demonstrated the capability of positively charged CMTs to remove negatively charged DTZ with a capacity of 11 mg g-1 from a 10 mg L-1 DTZ solution. These results reveal the potential of the as-designed module to serve as an oxidation unit coupled with other separation techniques, e.g., electro-adsorption or membrane processes.

Hands-on experience in the laboratory is essential in chemical engineering education to enhance the understanding of abstract theories and their effect on chemical processes. In this work, we describe a laboratory class, which combines some of the main engineering concepts into a set of hands-on experiments and simulations. Students are introduced to an iodine clock reaction performed in multiple different reactor types and are instructed to determine the reaction kinetics. Subsequent analysis of the experimental data in Python teaches basic programming skills and the concepts of numeric integration and optimization. Finally, a digital twin of one of the reactors is developed in COMSOL Multiphysics to give the students an application-focused introduction to more-dimensional multiphysics modeling. The students thereby get practical insights into the different methods and stages of reactor and reaction engineering. Based on the students' assignments, we consistently see a deeper understanding of reaction kinetics and reactor engineering than in the accompanying traditional lecture.

Conductive polymer devices with tunable resistance allow low‐energy, linear programming for efficient neuromorphic computing. Depolarizing impurities, however, are difficult to exclude and limit device performance through nonideal writes and self‐discharge. It is shown that these phenomena can be numerically described by combining two‐phase charge transport models with electrochemical self‐discharge. The simulations accurately reproduce the experimental data, including cyclic voltammetry and standard neuromorphic functions, such as linear programming of discrete states and short‐term potentiation. Impurities affect device write accuracy significantly for long programming times above 1000 ms. The effect is reduced to 0.03% for shorter times. Self‐discharge is impacted by device potential as well as impurity concentration. A model‐based trade‐off between operating parameters nearly triples the number of usable conductance states at ambient conditions. Understanding these device limitations as well as workarounds is a vital step toward the implementation of neuromorphic device networks.

Due to its biocompatibility, electrical conductivity, and tissue‐like elasticity, poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) constitutes a highly promising material regarding the fabrication of smart cell culture substrates. However, until now, high‐throughput synthesis of pure PEDOT:PSS geometries was restricted to flat sheets and fibers. In this publication, the first microfluidic process for the synthesis of spherical, highly porous, pure PEDOT:PSS particles of adjustable material properties is presented. The particles are synthesized by the generation of PEDOT:PSS emulsion droplets within a 1‐octanol continuous phase and their subsequent coagulation and partial crystallization in an isopropanol (IPA)/sulfuric acid (SA) bath. The process allows to tailor central particle characteristics such as crystallinity, particle diameter, pore size as well as electrochemical and mechanical properties by simply adjusting the IPA:SA ratio during droplet coagulation. To demonstrate the applicability of PEDOT:PSS particles as potential cell culture substrate, cultivations of L929 mouse fibroblast cells and MRC‐5 human fibroblast cells are conducted. Both cell lines present exponential growth on PEDOT:PSS particles and reach confluency with cell viabilities above 95 vol.% on culture day 9. Single cell analysis could moreover reveal that mechanotransduction and cell infiltration can be controlled by the adjustment of particle crystallinity.

Organic neuromorphic device networks can accelerate neural network algorithms and directly integrate with microfluidic systems or living tissues. Proposed devices based on the bio-compatible conductive polymer PEDOT:PSS have shown high switching speeds and low energy demand. However, as electrochemical systems, they are prone to self-discharge through parasitic electrochemical reactions. Therefore, the network’s synapses forget their trained conductance states over time. This work integrates single-device high-resolution charge transport models to simulate entire neuromorphic device networks and analyze the impact of self-discharge on network performance. Simulation of a single-layer nine-pixel image classification network commonly used in experimental demonstrations reveals no significant impact of self-discharge on training efficiency. And, even though the network’s weights drift significantly during self-discharge, its predictions remain 100% accurate for over ten hours. On the other hand, a multi-layer network for the approximation of the circle function is shown to degrade significantly over twenty minutes with a final mean-squared-error loss of 0.4. We propose to counter the effect by periodically reminding the network based on a map between a synapse’s current state, the time since the last reminder, and the weight drift. We show that this method with a map obtained through validated simulations can reduce the effective loss to below 0.1 even with worst-case assumptions. Finally, while the training of this network is affected by self-discharge, a good classification is still obtained. Electrochemical organic neuromorphic devices have not been integrated into larger device networks. This work predicts their behavior under nonideal conditions, mitigates the worst-case effects of parasitic self-discharge, and opens the path toward implementing fast and efficient neural networks on organic neuromorphic hardware.

During the first wave of Covid-19 infections in Germany in April 2020, clinics reported a shortage of filtering face masks with aerosol retention> 94% (FFP2 & 3, KN95, N95). Companies all over the world increased their production capacities, but quality control of once-certified materials and masks came up short. To help identify falsely labeled masks and ensure safe protection equipment, we tested 101 different batches of masks in 993 measurements with a self-made setup based on DIN standards. An aerosol generator provided a NaCl test aerosol which was applied to the mask. A laser aerosol spectrometer measured the aerosol concentration in a range from 90 to 500 nm to quantify the masks' retention. Of 101 tested mask batches, only 31 batches kept what their label promised. Especially in the initial phase of the pandemic in Germany, we observed fluctuating mask qualities. Many batches show very high variability in aerosol retention. In addition, by measuring with a laser aerosol spectrometer, we were able to show that not all masks filter small and large particles equally well. In this study we demonstrate how important internal and independent quality controls are, especially in times of need and shortage of personal protection equipment.

Operating electrochemical membrane processes beyond the limiting current density bears the potential to decrease the investment cost of desalination plants significantly. However, while there are strategies for successfully reducing energy demand by shortening the plateau region, their influence on the formation of electroconvection is still unknown. This study demonstrates control over the electroconvective vortices' rotational direction and position using a surface patterning method. We compare the development of electroconvection at two membranes modified with patterns of different surface charges. We analyze the electroconvective vortex field's build-up, the vortices' rotational direction, and structural stability in the steady-state. Finally, we showcase the control possibilities by enforcing a specific structure along an asymmetric letter pattern. Such tailor-made patterns have the potential to diminish the plateau region's energy loss completely. Furthermore, the scale-up of these membranes to industrial processes will allow the economic operation in the overlimiting regime, significantly increasing their space-time yield.

During the first wave of Covid-19 infections in Germany in April 2020, clinics reported a shortage of filtering face masks with aerosol retention > 94% (FFP2 & 3, KN95, N95). Companies all over the world increased their production capacities, but the quality control of once-certified materials and masks came up short. To help identify falsely labeled masks and ensure safe protection equipment, we tested 101 different batches of masks in 993 measurements with a self-made setup based on DIN standards. An aerosol generator provided a NaCl test aerosol which was applied to the mask. A laser aerosol spectrometer measured the aerosol concentration in a range from 90 to 500 nm to quantify the masks' retention. Of 101 tested mask batches, only 31 batches kept what their label promised. Especially in the initial phase of the pandemic in Germany, we observed fluctuating mask qualities. Many batches show very high variability in aerosol retention. In addition, by measuring with a laser aerosol spectrometer, we were able to show that not all masks filter small and large particles equally well. In this study, we demonstrate how important internal and independent quality controls are, especially in times of need and shortage of personal protection equipment.

The electrochemical synthesis of hydrogen peroxide (H2O2) using the oxygen reduction reaction (ORR) requires highly catalytic active, selective, and stable electrode materials to realize a green and efficient process. The present publication shows for the first time the application of a facile one-step bottom-up wet-spinning approach for the continuous fabrication of stable and flexible tubular poly(3,4-ethylene dioxythiophene) (PEDOT : PSS) and PEDOT : PSS/carbon nanotube (CNT) hollow fibers. Additionally, electrochemical experiments reveal the catalytic activity of acid-treated PEDOT : PSS and its composites in the ORR forming hydrogen peroxide for the first time. Under optimized conditions, the composite electrodes with 40 wt % CNT loading could achieve a high production rate of 0.01 mg/min/cm2 and a current efficiency of up to 54 %. In addition to the high production rate, the composite hollow fiber has proven its long-term stability with 95 % current retention after 20 h of hydrogen peroxide production.

Stimuli-responsive polymers are capable of reacting to an external trigger. We report self-regulated, enzymatically active, and pH-responsive hydrogels that show dynamic behavior without an external trigger. This is enabled by a feedback loop between the enzymatic conversion of glucose into gluconic acid and the pH-induced volume phase transition that leads to a modulation in glucose permeability. The synthesized hydrogel spheres combine all required properties for sustained oscillation including enzymatic activity, switchable reactivity, hysteresis in volume phase transition and feedback between the reaction and permeation. A simple model of the system identified possible operating points where sustained oscillations are possible. Experiments at these operating points revealed that the system is able to perform a self-regulated oscillation cycle under a constant nutrient supply. A sensitivity analysis showed that the system is especially sensitive around the point of oscillation, so that precise control of the process parameters is essential to achieve sustained oscillations.

Two-photon vertical-flow lithography is demonstrated for synthesis of complex-shaped polymeric microtubes with a high aspect ratio (>100:1). This unique microfluidic approach provides rigorous control over the morphology and surface topology to generate thin-walled (<1 µm) microtubes with a tunable diameter (1-400 µm) and pore size (1-20 µm). The interplay between fluid-flow control and two-photon lithography presents a generic high-resolution method that will substantially contribute toward the future development of biocompatible scaffolds, stents, needles, nerve guides, membranes, and beyond.