Hybrid thermionic-photovoltaic converter with graphene-on-semiconductor heterojunction anode for efficient electricity generation

Thermionic energy converters are solid-state heat engines to produce electricity with significant potential, whereas the output voltage is constrained by the work function difference between cathode and anode. In this work, we originally apply a graphene-on-semiconductor heterojunction anode to a thermionic-photovoltaic (TIPV) converter to output additional voltage. Thermionic electrons are injected into the graphene layer and then recombined with photogenerated holes. Photogenerated electrons are extracted from the conduction band and reinjected into the cathode through an external load. A proof-of-concept demonstration of the TIPV converter is developed with barium surface-engineered cathode and anode. Open-circuit voltage is increased from ∼0.9 to ∼1.9 V by comparing with an identical configuration without graphene layer. The TIPV converter yields a power generation density of 2.7 kW/m² with an electronic efficiency of ∼27%. This work paves the way for the development of TIPV converter toward high power density.

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A novel thermionic energy converter is originally proposed and measured

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Open-circuit voltage is increased from 0.9 to 1.9 V by the photovoltaic effect

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A power density of 2.7 kW/m² with an electronic efficiency of ∼27% is measured

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Ultralow work function is obtained by forming barium surface dipoles

Modeling fine-grained spatio-temporal pollution maps with low-cost sensors

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.

Materials and devices for immersive virtual reality

The metaverse may change the way we live and interact with one another, and its potential applications range from entertainment to health care. Extended reality is the main technology to realize the highly realistic, interactive and immersive metaverse experience, and wearable electronic devices and materials are at its core.

Shock-absorbing effect of flooring-adopted mechanical metamaterial technology and its influence on the gait and balance of older adults

Objective

To elucidate the performance of a shock-absorbing floor material with a mechanical metamaterial (MM-flooring) structure and its effect on the gait and balance of older adults.

Methods

The drop-weight impact was applied to evaluate the shock-absorbing performance. The falling weight was adjusted equivalent to the energy exerted on the femur of an older woman when she falls, which was evaluated on the MM-flooring and six other flooring materials.

Nineteen healthy people over the age of 65 years participated in the gait and balance evaluations. The timed up and go and two-step tests were adopted as gait performance tests, and the sway-during-quiet-balance test with force plates and the functional reach test (FRT) were adopted as balance tests. All the participants underwent these tests on the MM-flooring, shock-absorbing mat and rigid flooring.

Results

The shock-absorbing performance test revealed that MM-flooring has sufficient shock-absorbing performance, and suggesting that it may reduce the probability of fractures in the older people when they fall. The results of the gait performance test showed that the participants demonstrated the same gait performance on the MM-flooring and the rigid floor. In the quiet standing test, MM-flooring did not affect the balance function of the participants to the same extent as the rigid floor, compared with the shock-absorbing mat. In the FRT, no significant differences were found for any of the flooring conditions.

Conclusions

MM-flooring has the potential to prevent fractures attributed to falls and does not affect the gait or balance of older adults.

Enhancing nitrogen removal through directly integrating anammox into mainstream wastewater treatment: Advantageous, issues and future study

Anaerobic ammonium oxidation (anammox) has great potential to be applied to the process of nitrogen removal from mainstream wastewater. However, directly applying complete anammox to the mainstream is typically hindered by low temperatures, a low ammonia concentration, and high organic matter concentrations. Directly integrating anammox into mainstream treatment by enhancing the in-situ enrichment of anammox bacteria in wastewater treatment plants (WWTPs) could effectively improve the nitrogen removal efficiency and reduce the treatment cost. A certain anammox bacteria abundance in full-scale WWTPs provides the feasibility of directly integrating anammox into mainstream treatment and realizing partial mainstream anammox. The technical development status of partial anammox and the mechanisms of achieving partial mainstream anammox by aeration and organic control are summarized. This review provides an enhanced understanding of this novel technical route of partial mainstream anammox treatment for improving the quality, performance, and prospects for this technology to be used in upgrading WWTPs.

Measurement of oxygen consumption rates of human renal proximal tubule cells in an array of organ-on-chip devices to monitor drug-induced metabolic shifts

Measurement of cell metabolism in moderate-throughput to high-throughput organ-on-chip (OOC) systems would expand the range of data collected for studying drug effects or disease in physiologically relevant tissue models. However, current measurement approaches rely on fluorescent imaging or colorimetric assays that are focused on endpoints, require labels or added substrates, and lack real-time data. Here, we integrated optical-based oxygen sensors in a high-throughput OOC platform and developed an approach for monitoring cell metabolic activity in an array of membrane bilayer devices. Each membrane bilayer device supported a culture of human renal proximal tubule epithelial cells on a porous membrane suspended between two microchannels and exposed to controlled, unidirectional perfusion and physiologically relevant shear stress for several days. For the first time, we measured changes in oxygen in a membrane bilayer format and used a finite element analysis model to estimate cell oxygen consumption rates (OCRs), allowing comparison with OCRs from other cell culture systems. Finally, we demonstrated label-free detection of metabolic shifts in human renal proximal tubule cells following exposure to FCCP, a drug known for increasing cell oxygen consumption, as well as oligomycin and antimycin A, drugs known for decreasing cell oxygen consumption. The capability to measure cell OCRs and detect metabolic shifts in an array of membrane bilayer devices contained within an industry standard microtiter plate format will be valuable for analyzing flow-responsive and physiologically complex tissues during drug development and disease research.

Multilayer CVD graphene electrodes using a transfer-free process for the next generation of optically transparent and MRI-compatible neural interfaces

Multimodal platforms combining electrical neural recording and stimulation, optogenetics, optical imaging, and magnetic resonance (MRI) imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific research. Electrically conductive, optically transparent, and MRI-compatible electrodes can optimally combine all modalities. Graphene as a suitable electrode candidate material can be grown via chemical vapor deposition (CVD) processes and sandwiched between transparent biocompatible polymers. However, due to the high graphene growth temperature (≥ 900 °C) and the presence of polymers, fabrication is commonly based on a manual transfer process of pre-grown graphene sheets, which causes reliability issues. In this paper, we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural interfaces. Our fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and geometry. They also exhibit the highest charge storage capacity (CSC) reported to date among all previously fabricated CVD graphene electrodes. Our graphene electrodes did not reveal any photo-induced artifact during 10-Hz light pulse illumination. Additionally, we show here, for the first time, that CVD graphene electrodes do not cause any image artifact in a 3T MRI scanner. These results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal electrodes. Our fabricated graphene electrodes enable multimodal neural recording, electrical and optogenetic stimulation, while allowing for optical imaging, as well as, artifact-free MRI studies.

Strain-tunable optical microlens arrays with deformable wrinkles for spatially coordinated image projection on a security substrate

As a new concept in materials design, a variety of strategies have been developed to fabricate optical microlens arrays (MLAs) that enable the miniaturization of optical systems on the micro/nanoscale to improve their characteristic performance with unique optical functionality. In this paper, we introduce a cost-effective and facile fabrication process on a large scale up to ~15 inches via sequential lithographic methods to produce thin and deformable hexagonally arranged MLAs consisting of polydimethylsiloxane (PDMS). Simple employment of oxygen plasma treatment on the prestrained MLAs effectively harnessed the spontaneous formation of highly uniform nanowrinkled structures all over the surface of the elastomeric microlenses. With strain-controlled tunability, unexpected optical diffraction patterns were characterized by the interference combination effect of the microlens and deformable nanowrinkles. Consequently, the hierarchically structured MLAs presented here have the potential to produce desirable spatial arrangements, which may provide easily accessible opportunities to realize microlens-based technology by tunable focal lengths for more advanced micro-optical devices and imaging projection elements on unconventional security substrates.

Fictional Film in Engineering Ethics Education: With Miyazaki's The Wind Rises as Exemplar

This paper aims to call attention to the potential of using film in engineering ethics education, which has not been thoroughly discussed as a pedagogical method in this field. A review of current approaches to teaching engineering ethics reveals that there are both learning outcomes that need more attention as well as additional pedagogical methods that could be adopted. Scholarship on teaching with film indicates that film can produce ethical experiences that go beyond those produced by both conventional methods of teaching engineering ethics and more arts-based methods such as fiction, as well as connect ethics learning outcomes and issues to the lifeworld of a person. The paper further illustrates the potential of using Miyazaki Hayao's film The Wind Rises for highlighting a range of ethical issues pertaining to engineering. It also discusses the important role educators play in how film can be used effectively in the classroom. Synthesizing a range of sources from film theory to the use of film in business and medical education, the paper makes the case for using film in engineering ethics education and calls for more research on the use of this method.

Highly efficient passive Tesla valves for microfluidic applications

A multistage optimization method is developed yielding Tesla valves that are efficient even at low flow rates, characteristic, e.g., for almost all microfluidic systems, where passive valves have intrinsic advantages over active ones. We report on optimized structures that show a diodicity of up to 1.8 already at flow rates of 20 μl s^{- 1} corresponding to a Reynolds number of 36. Centerpiece of the design is a topological optimization based on the finite element method. It is set-up to yield easy-to-fabricate valve structures with a small footprint that can be directly used in microfluidic systems. Our numerical two-dimensional optimization takes into account the finite height of the channel approximately by means of a so-called shallow-channel approximation. Based on the three-dimensionally extruded optimized designs, various test structures were fabricated using standard, widely available microsystem manufacturing techniques. The manufacturing process is described in detail since it can be used for the production of similar cost-effective microfluidic systems. For the experimentally fabricated chips, the efficiency of the different valve designs, i.e., the diodicity defined as the ratio of the measured pressure drops in backward and forward flow directions, respectively, is measured and compared to theoretical predictions obtained from full 3D calculations of the Tesla valves. Good agreement is found. In addition to the direct measurement of the diodicities, the flow profiles in the fabricated test structures are determined using a two-dimensional microscopic particle image velocimetry (μPIV) method. Again, a reasonable good agreement of the measured flow profiles with simulated predictions is observed.

Recent advances in acoustofluidic separation technology in biology

Acoustofluidic separation of cells and particles is an emerging technology that integrates acoustics and microfluidics. In the last decade, this technology has attracted significant attention due to its biocompatible, contactless, and label-free nature. It has been widely validated in the separation of cells and submicron bioparticles and shows great potential in different biological and biomedical applications. This review first introduces the theories and mechanisms of acoustofluidic separation. Then, various applications of this technology in the separation of biological particles such as cells, viruses, biomolecules, and exosomes are summarized. Finally, we discuss the challenges and future prospects of this field.

Microbial engineering for the production and application of phytases to the treatment of the toxic pollutants: A review

Phytases are a group of digestive enzymes which are commonly used as feed enzymes. These enzymes are used exogenously in the feeds of monogastric animals thereby it improves the digestibility of phosphorous and thus reduces the negative impact of inorganic P excretion on the environment. Even though these enzymes are widely distributed in many life forms, microorganisms are the most preferred and potential source of phytase. Despite the extensive availability of the phytase-producing microbial consortia, only a few microorganisms have been known to be exploited at industrial level. The high costs of the enzyme along with the incapability to survive high temperatures followed by the poor storage stability are noted to be the bottleneck in the commercialization of enzymes. For this reason, besides the conventional fermentation approaches, the applicability of cloning, expression studies and genetic engineering has been implemented for the past few years to accomplish the abovesaid benefits. The site-directed mutagenesis as well as knocking out have also validated their prominent role in microbe-based phytase production with enhanced levels. The present review provides detailed information on recent insights on the modification of phytases through heterologous expression and protein engineering to make thermostable and protease-resistant phytases.

Deep learning for non-parameterized MEMS structural design

The geometric designs of MEMS devices can profoundly impact their physical properties and eventual performances. However, it is challenging for researchers to rationally consider a large number of possible designs, as it would be very time- and resource-consuming to study all these cases using numerical simulation. In this paper, we report the use of deep learning techniques to accelerate the MEMS design cycle by quickly and accurately predicting the physical properties of numerous design candidates with vastly different geometric features. Design candidates are represented in a nonparameterized, topologically unconstrained form using pixelated black-and-white images. After sufficient training, a deep neural network can quickly calculate the physical properties of interest with good accuracy without using conventional numerical tools such as finite element analysis. As an example, we apply our deep learning approach in the prediction of the modal frequency and quality factor of disk-shaped microscale resonators. With reasonable training, our deep learning neural network becomes a high-speed, high-accuracy calculator: it can identify the flexural mode frequency and the quality factor 4.6 × 10³ times and 2.6 × 10⁴ times faster, respectively, than conventional numerical simulation packages, with good accuracies of 98.8 ± 1.6% and 96.8 ± 3.1%, respectively. When simultaneously predicting the frequency and the quality factor, up to ~96.0% of the total computation time can be saved during the design process. The proposed technique can rapidly screen over thousands of design candidates and promotes experience-free and data-driven MEMS structural designs.

Human injury-based safety decision of automated vehicles

Automated vehicles (AVs) are anticipated to improve road traffic safety. However, prevailing decision-making algorithms have largely neglected the potential to mitigate injuries when confronting inevitable obstacles. To explore whether, how, and to what extent AVs can enhance human protection, we propose an injury risk mitigation-based decision-making algorithm. The algorithm is guided by a real-time, data-driven human injury prediction model and is assessed using detailed first-hand information collected from real-world crashes. The results demonstrate that integrating injury prediction into decision-making is promising for reducing traffic casualties. Because safety decisions involve harm distribution for different participants, we further analyze the potential ethical issues quantitatively, providing a technically critical step closer to settling such dilemmas. This work demonstrates the feasibility of applying mining tools to identify the underlying mechanisms embedded in crash data accumulated over time and opens the way for future AVs to facilitate optimal road traffic safety.

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We propose an injury risk mitigation-based decision-making algorithm for AVs

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A real-time, data-driven human injury prediction model was established

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We applied mining tools to identify mechanisms embedded in accumulated crash data

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We analyzed traffic ethical issues quantitatively, closer to feasible solutions