The inadequacy of animal models in correctly predicting drug and biothreat agent toxicity in humans has resulted in a pressing need for in vitro models that can recreate the in vivo scenario. One of the most important organs in the assessment of drug toxicity is liver. Here, we report the development of a liver-on-a-chip platform for long-term culture of three-dimensional (3D) human HepG2/C3A spheroids for drug toxicity assessment. The bioreactor design allowed for in situ monitoring of the culture environment by enabling direct access to the hepatic construct during the experiment without compromising the platform operation. The engineered bioreactor could be interfaced with a bioprinter to fabricate 3D hepatic constructs of spheroids encapsulated within photocrosslinkable gelatin methacryloyl (GelMA) hydrogel. The engineered hepatic construct remained functional during the 30 days culture period as assessed by monitoring the secretion rates of albumin, alpha-1 antitrypsin, transferrin, and ceruloplasmin, as well as immunostaining for the hepatocyte markers, cytokeratin 18, MRP2 bile canalicular protein and tight junction protein ZO-1. Treatment with 15 mM acetaminophen induced a toxic response in the hepatic construct that was similar to published studies on animal and other in vitro models, thus providing a proof-of-concept demonstration of the utility of this liver-on-a-chip platform for toxicity assessment.

A highly elastic hybrid hydrogel of methacryloyl‐substituted recombinant human tropoelastin (MeTro) and graphene oxide (GO) nanoparticles are developed. The synergistic effect of these two materials significantly enhances both ultimate strain (250%), reversible rotation (9700°), and the fracture energy (38.8 ± 0.8 J m−2) in the hybrid network. Furthermore, improved electrical signal propagation and subsequent contraction of the muscles connected by hybrid hydrogels are observed in ex vivo tests.

In integrated photonics, specific wavelengths are preferred such as 1550 nm due to low-loss transmission and the availability of optical gain in this spectral region. For chip-based photodetectors, layered two-dimensional (2D) materials bear scientific and technologicallyrelevant properties such as electrostatic tunability and strong light-matter interactions. However, no efficient photodetector in the telecommunication C-band has been realized with 2D transition metal dichalcogenide (TMDCs) materials due to their large optical bandgap. Here, we demonstrate a MoTe2-based photodetector featuring strong photoresponse (responsivity = 0.5 A/W) operating at 1550 nm on silicon micro ring resonator enabled by strain engineering of the transition-metal-dichalcogenide film. We show that an induced tensile strain of ~4% reduces the bandgap of MoTe2, resulting in large photo-response in the telecommunication wavelength, in otherwise photo-inactive medium when unstrained. Unlike Graphene-based photodetectors relying on a gapless band structure, this semiconductor-2D material detector shows a ~100X improved dark current enabling an efficient noise-equivalent power of just 90 pW/Hz 0.5 . Such strain-engineered integrated photodetector provides new opportunities for integrated optoelectronic systems.

Muscle-based biohybrid actuators have generated significant interest as the future of biorobotics but so far they move without having much control over their actuation behavior. Integration of microelectrodes into the backbone of these systems may enable guidance during their motion and allow precise control over these actuators with specific activation patterns. Here, we addressed this challenge by developing aligned CNT forest microelectrode arrays and incorporated them into scaffolds for stimulating the cells. Aligned CNTs were successfully embedded into flexible and biocompatible hydrogel exhibiting excellent anisotropic electrical conductivity. Bioactuators were then engineered by culturing cardiomyocytes on the CNT microelectrode-integrated hydrogel constructs. The resulting cardiac tissue showed homogeneous cell organization with improved cell-to-cell coupling and maturation, which was directly related to the contractile force of muscle tissue. This centimeter-scale bioactuator has excellent mechanical integrity, embedded microelectrodes and is capable of spontaneous actuation behavior. Furthermore, we demonstrated that a biohybrid machine can be controlled by an external electrical field provided by the integrated CNT microelectrode arrays. In addition, due to the anisotropic electrical conductivity of the electrodes provided from aligned CNTs, significantly different excitation thresholds were observed in different configurations such as the ones in parallel vs. perpendicular direction to the CNT alignment.

Graphene has extraordinary electro-optic properties and is therefore a promising candidate for monolithic photonic devices such as photodetectors. However, the integration of this atom-thin layer material with bulky photonic components usually results in a weak light-graphene interaction leading to large device lengths limiting electro-optic performance.In contrast, here we demonstrate a plasmonic slot graphene photodetector on silicon-oninsulator platform with high-responsivity given the 5 µm-short device length. We observe that the maximum photocurrent, and hence the highest responsivity, scales inversely with the slot gap width. Using a dual-lithography step, we realize 15 nm narrow slots that show a 15-times higher responsivity per unit device-length compared to photonic graphene photodetectors. Furthermore, we reveal that the back-gated electrostatics is overshadowed by channel-doping contributions induced by the contacts of this ultra-short channel graphene photodetector. This leads to quasi charge neutrality, which explains both the previously-unseen offset between the maximum photovoltaic-based photocurrent relative to graphene's Dirac point and the observed non-ambipolar transport. Such micrometer compact and absorption-efficient photodetectors allow for short-carrier pathways in nextgeneration photonic components, while being an ideal testbed to study short-channel carrier physics in graphene optoelectronics. Introduction.Graphene has become a complementary platform for electronics and optoelectronics because of its remarkable properties and versatility(1). A variety of applications exploit graphene's peculiar features to include modulators(2), plasmonic optoelectronics(3-6), photovoltaic devices (7), ultrafast lasers (8), and photo-detection(9, 10). For photo conversion applications the linear and gap-less band structure of graphene results in wavelength-independent absorption (11,12).Moreover, graphene's carrier can be tuned via electrostatically doping, thus modulating light absorption. Due to its superb carrier mobility (13,14), graphene-based absorption enables ultrafast conversion of photons or plasmons to electrical currents or voltages. However, the light-graphene interaction, and consequently the responsivity of graphene-based devices, is usually rather weak due to the geometrical mismatch between graphene's atom-thin thickness and the diffractionlimited optical mode area of photonic components.The first-generation of graphene-based free-space photodetectors (PDs) uses metal-graphenemetal structures(14); choosing different work-functions for the source-and drain contacts results in an asymmetric band structure, thus enabling non-biased band-bending for charge polarity separation, leading to near-zero dark current. Interdigitated metallic contacts, are typically adopted Corresponding AuthorVolker Sorger, sorger@gwu.edu Funding SourcesVS is funded by AFOSR (FA9550-17-1-0377) and ARO (W911NF-16-2-0194).

With the recent successes of neural networks (NN) to perform machine-learning tasks, photonic-based NN designs may enable high throughput and low power neuromorphic compute paradigms since they bypass the parasitic charging of capacitive wires. Thus, engineering data-information processors capable of executing NN algorithms with high efficiency is of major importance for applications ranging from pattern recognition to classification. Our hypothesis is therefore, that if the time-limiting electro-optic conversion of current photonic NN designs could be postponed until the very end of the network, then the execution time of the photonic algorithm is simple the delay of the time-of-flight of photons through the NN, which is on the order of picoseconds for integrated photonics. Exploring such all-optical NN, in this work we discuss two independent approaches of implementing the optical perceptron's nonlinear activation function based on nanophotonic structures exhibiting i) induced transparency and ii) reverse saturated absorption. Our results show that the all-optical nonlinearity provides about 3 and 7 dB extinction ratio for the two systems considered, respectively, and classification accuracies of an exemplary MNIST task of 97% and near 100% are found, which rivals that of software based trained NNs, yet with ignored noise in the network. Together with a developed concept for an all-optical perceptron, these findings point to the possibility of realizing pure photonic NNs with potentially unmatched throughput and even energy consumption for next generation information processing hardware.

Myocardial microenvironment plays a decisive role in guiding the function and fate of cardiomyocytes, and engineering this extracellular niche holds great promise for cardiac tissue regeneration. Platforms utilizing hybrid hydrogels containing various types of conductive nanoparticles have been a critical tool for constructing engineered cardiac tissues with outstanding mechanical integrity and improved electrophysiological properties. However, there has been no attempt to directly compare the efficacy of these hybrid hydrogels and decipher the mechanisms behind how these platforms differentially regulate cardiomyocyte behavior. Here, we employed gelatin methacryloyl (GelMA) hydrogels containing three different types of carbon-based nanoparticles: carbon nanotubes (CNTs), graphene oxide (GO), and reduced GO (rGO), to investigate the influence of these hybrid scaffolds on the structural organization and functionality of cardiomyocytes. Using immunofluorescent staining for assessing cellular organization and proliferation, we showed that electrically conductive scaffolds (CNT- and rGO-GelMA compared to relatively nonconductive GO-GelMA) played a significant role in promoting desirable morphology of cardiomyocytes and elevated the expression of functional cardiac markers, while maintaining their viability. Electrophysiological analysis revealed that these engineered cardiac tissues showed distinct cardiomyocyte phenotypes and different levels of maturity based on the substrate (CNT-GelMA: ventricular-like, GO-GelMA: atrial-like, and rGO-GelMA: ventricular/atrial mixed phenotypes). Through analysis of gene-expression patterns, we uncovered that the engineered cardiac tissues matured on CNT-GelMA and native cardiac tissues showed comparable expression levels of maturation markers. Furthermore, we demonstrated that engineered cardiac tissues matured on CNT-GelMA have increased functionality through integrin-mediated mechanotransduction (via YAP/TAZ) in contrast to cardiomyocytes cultured on rGO-GelMA.

Machine intelligence has become a driving factor in modern society. However, its demand outpaces the underlying electronic technology due to limitations given by fundamental physics, such as capacitive charging of wires, but also by system architecture of storing and handling data, both driving recent trends toward processor heterogeneity. Task-specific accelerators based on free-space optics bear fundamental homomorphism for massively parallel and real-time information processing given the wave nature of light. However, initial results are frustrated by data handling challenges and slow optical programmability. Here we introduce a novel amplitude-only Fourier-optical processor paradigm capable of processing large-scale ∼ ( 1000 × 1000 ) matrices in a single time step and 100 µs-short latency. Conceptually, the information flow direction is orthogonal to the two-dimensional programmable network, which leverages 10 6 parallel channels of display technology, and enables a prototype demonstration performing convolutions as pixelwise multiplications in the Fourier domain reaching peta operations per second throughputs. The required real-to-Fourier domain transformations are performed passively by optical lenses at zero-static power. We exemplary realize a convolutional neural network (CNN) performing classification tasks on 2 megapixel large matrices at 10 kHz rates, which latency-outperforms current graphic processing unit and phase-based display technology by 1 and 2 orders of magnitude, respectively. Training this optical convolutional layer on image classification tasks and utilizing it in a hybrid optical-electronic CNN, shows classification accuracy of 98% (Modified National Institute of Standards and Technology) and 54% (CIFAR-10). Interestingly, the amplitude-only CNN is inherently robust against coherence noise in contrast to phase-based paradigms and features a delay over 2 orders of magnitude lower than liquid-crystal-based systems. Such an amplitude-only massively parallel optical compute paradigm shows that the lack of phase information can be accounted for via training, thus opening opportunities for high-throughput accelerator technology for machine intelligence with applications in network-edge processing, in data centers, or in pre-processing information or filtering toward near-real-time decision making.