Weight Constraints Research Articles

The potential of using 3D printing in the manufacture of mini hydraulic systems

The work is devoted to an analytical engineering study of the practicality of using 3D printing for the manufacture of hydraulic components, in order to solve the problems associated with weight, size and complexity inherent in traditional hydraulic elements. The research aims to demonstrate the feasibility of using 3D printing to achieve simplified design and increased efficiency. The work presents samples of hydraulic elements printed on a 3D printer, which reflects the practical feasibility of this approach. A simple and practical design methodology is proposed for two key components of a typical hydraulic system, namely, a hydraulic cylinder and a directional servo-valve, which allows hydraulic actuators to be controlled using any programmable microcontroller. A distinctive feature of this approach is the emphasis on the principles of compact design, which facilitates the integration of many parts into single, multifunctional part. In addition, the use of lightweight materials in 3D printing helps reduce the overall weight of hydraulic components. The proposed method also allows the integration of various hydraulic elements into a single integral structure printed on a 3D printer, offering a pragmatic solution to the size and complexity issues associated with traditional metal hydraulic systems. The scope of this innovative approach extends to the creation of prototypes of hydraulic and pneumatic robots, as well as devices that require compactness, precision and adaptability. This methodology is promising for implementation in the field of robotics, especially for tasks where space and weight constraints are critical factors.

Pallets delivery: Two matheuristics for combined loading and routing

The implementation of novel regulatory and technical requirements for the distribution of vehicle axle weights in road freight transport introduces a new set of constraints on vehicle routing. Until now, axle weight distribution in determining the load plan for freight transport units has been overlooked in the vehicle routing process. Compliance with these axle weight constraints has become paramount for road freight transport companies, since noncompliance with the axle weight distribution legislation translates into heavy fines. This work aims to provide a tool capable of generating cargo loading plans and routing sequences for a palletised cargo distribution problem. The problem addressed integrates the capacitated vehicle routing problem with time window and the two-dimensional loading problem with load balance constraints. Two integrative solution approaches are proposed, one giving greater importance to the routing and the other prioritising the loading. In addition, a novel MILP model is proposed for the 2D pallet loading problem with load-balance constraints that take advantage of the standard dimension of the pallets. Extensive computational experiments were performed with a set of well-known literature benchmark instances, extended to incorporate additional features. The computational results show the effectiveness of the proposed approaches.

MULTI-LEVEL CITY PORTRAIT RESEARCH BASED ON MULTI-SOURCE DATA

Abstract. City portrait is a social impression generated by the interaction between the public and the city, which can help us better understand and perceive the nature and characteristics of the city, and thus provide strong support for the development and governance of the city. However, most existing studies extract thematic semantic labels globally, but ignore the order of the tags and the degree of their contribution in the topic, which affects the city portrait extraction results. In addition, existing studies also lack the analysis of the impact of grid areas as the study scale on city portraits. In this paper, we propose a new approach to accurately identify city labels based on multi-source data grid fusion using a topic feature word extraction model (Weight-LdaVecNet) with fused topic word embedding and network structure analysis with feature word weight constraints. On this basis, we construct a multi-level city portrait description framework using hierarchical cluster analysis, extract tag clusters, and obtain a similarity matrix by combining topic feature tags and region feature tags using similarity analysis to construct a multi-level city region portrait, with a view to achieving a fine-grained construction of a multi-level city portrait. The experimental results show that, compared with the traditional LDA model, our method indicates that the identified city labels with similar thematic semantics have strong aggregation, thus proving the effectiveness of our proposed method. In addition, in the overall multi-level city portrait, we find that Beijing has a strong attractiveness in terms of cultural features. However, the regional distribution of cultural characteristics dimensions is not uniform in the multilevel city-region portrait, and better rational allocation and planning of cultural resources are needed to better meet people's needs.

Uncrewed Aerial Spray Systems For Mosquito Control: Efficacy Studies For Space Sprays.

Achieving an appropriate droplet size distribution for adulticiding has proved problematic for unmanned aerial spray systems (UASSs). The high-pressure pumping systems utilized on crewed aircraft conflict with the weight constraints of UASSs. The alternative is a lightweight rotary atomizer, which when run at a maximum rpm with a minimal flow rate can achieve the appropriate droplet size distribution. For this study a UASS was calibrated to discharge an appropriate droplet size distribution (Dv0.5 of 48 µm and Dv0.9 of 76 µm). Spray was released from an altitude of 23 m (75 ft). The spray plume was shown to effectively disperse through the sampling zone. To achieve the appropriate application rate, the flight speed was 3 m/sec (6.7 mph) with an assumed swath of 150 m (500 ft). The objective of this project was not to conduct an operational application; instead only 1 flight line was used so that the effective swath width could be confirmed and the appropriate flightline separation defined. This study showed that control was achieved across distances of 100-150 m. Considering a swath width of 150 m (500 ft), ground deposition was 13-36% of applied material. Spray deposition corresponded well with the mortality data, which helped improve confidence in the data. The overall conclusion from this study is that aerial adulticiding is feasible with the system presented here. Further work is required to improve the atomization system to allow operational flight speeds and to determine the interaction between release altitude and droplet size in order to minimize ground deposition of application material.

Truck-Drone Pickup and Delivery Problem with Drone Weight-Related Cost

Truck-drone delivery is widely used in logistics distribution for achieving sustainable development, in which drone weight greatly affects transportation cost. Thus, we consider a new combined truck-drone pickup and delivery problem with drone weight-related cost in the context of last-mile logistics. A system of integer programming is formulated with the objective of minimizing the total cost of the drone weight-related cost, fixed vehicle cost and travel distance cost. An improved adaptive large neighborhood search algorithm (IALNS) is designed based on the characteristics of the problem, several effective destroy and repair operators are designed to explore the solution space, and a simulated annealing strategy is introduced to avoid falling into the local optimal solution. To evaluate the performance of the IALNS algorithm, 72 instances are randomly generated and tested. The computational results on small instances show that the proposed IALNS algorithm performs better than CPLEX both in efficiency and effectiveness. When comparing the truck-drone pickup and delivery problem with drone weight-related cost to the problem without drone weight-related cost, it is found that ignoring the drone weight constraints leads to an underestimate of the total travel cost by 12.61% based on the test of large instances.

Conception of a compact flow boiling loop for the International Space Station- First results in parabolic flights

The design of a pipe flow boiling experiment for the International Space Station is proposed, taking into account typical weight, power consumption and size constraints. The effect of singularities such as elbows upstream the test section is investigated. Velocity profiles downstream two elbows, measured by Particle Image Velocimetry are in good agreement with numerical simulation and allow to determine a specific distance (decay length) downstream the elbows for which the velocity profile recover its axisymmetry. From these results a breadboard is designed and tested in parabolic flights. Care has been taken to generate boiling downstream the decay length. Two-phase bubbly flow is observed with 2 perpendicular high-speed cameras in the test section and a symmetry of the bubble distribution in the pipe is verified for different gravity conditions when the bubbles are created after the decay length.

UNCREWED AERIAL SPRAY SYSTEMS FOR MOSQUITO CONTROL: EFFICACY STUDIES FOR SPACE SPRAYS.

Achieving an appropriate droplet size distribution for adulticiding has proved problematic for unmanned aerial spray systems (UASSs). The high-pressure pumping systems utilized on crewed aircraft conflict with the weight constraints of UASSs. The alternative is a lightweight rotary atomizer, which when run at a maximum rpm with a minimal flow rate can achieve the appropriate droplet size distribution. For this study a UASS was calibrated to discharge an appropriate droplet size distribution (Dv0.5 of 48 µm and Dv0.9 of 76 µm). Spray was released from an altitude of 23 m (75 ft). The spray plume was shown to effectively disperse through the sampling zone. To achieve the appropriate application rate, the flight speed was 3 m/sec (6.7 mph) with an assumed swath of 150 m (500 ft). The objective of this project was not to conduct an operational application; instead only 1 flight line was used so that the effective swath width could be confirmed and the appropriate flightline separation defined. This study showed that control was achieved across distances of 100-150 m. Considering a swath width of 150 m (500 ft), ground deposition was 13-36% of applied material. Spray deposition corresponded well with the mortality data, which helped improve confidence in the data. The overall conclusion from this study is that aerial adulticiding is feasible with the system presented here. Further work is required to improve the atomization system to allow operational flight speeds and to determine the interaction between release altitude and droplet size in order to minimize ground deposition of application material.

Joint inversion of gravity and gravity gradient data using smoothed L0 norm regularization algorithm with sensitivity matrix compression

Improving efficiency and accuracy are critical issues in geophysical inversion. In this study, a new algorithm is proposed for the joint inversion of gravity and gravity gradient data. Based on the regularization theory, the objective function is constructed using smoothed L0 norm (SL0), then the optimal solution is obtained by the non-linear conjugate gradient method. Numerical modeling shows that our algorithm is much more efficient than the conventional SL0 based on the sparse theory, especially when inverting large-scale data, and also has better anti-noise performance while preserving its advantage of high accuracy. Compressing the sensitivity matrices has further improved efficiency, and introducing the data weighting and the self-adaptive regularization parameter has improved the convergence rate of the inversion. Moreover, the impacts of the depth weighting, model weighting, and density constraint are also analyzed. Finally, our algorithm is applied to the gravity and gravity gradient measurements at the Vinton salt dome. The inverted distribution range, thickness, and geometry of the cap rock are in good agreement with previous studies based on geological data, drilling data, seismic data, etc., validating the feasibility of this algorithm in actual geological conditions.

Load Factor Optimization for the Auto Carrier Loading Problem

The distribution of passenger vehicles is a complex task and a high cost factor for automotive original equipment manufacturers (OEMs). On the way from the production plant to the customer, vehicles travel long distances on different carriers, such as ships, trains, and trucks. To save costs, OEMs and logistics service providers aim to maximize their loading capacities. Modern auto carriers are extremely flexible. Individual platforms can be rotated, extended, or combined to accommodate vehicles of different shapes and weights and to nest them in a way that makes the best use of the available space. In practice, finding feasible combinations is done with the help of simple heuristics or based on personal experience. In research, most papers that deal with auto carrier loading focus on route or cost optimization. Only a rough approximation of the loading subproblem is considered. In this paper, we present two different methodologies to approximate realistic load factors considering the flexibility of modern auto carriers and their height, length, and weight constraints. Based on our industry partner’s process, the vehicle distribution follows a first in, first out principle. For the first approach, we formulate the problem as a mixed integer, quadratically constrained assignment problem. The second approach considers the problem as a two-dimensional nesting problem with irregular shapes. We perform computational experiments using real-world data from a large German automaker to validate and compare both models with each other and with an approximate model adapted from the literature. The simulation results for the first approach show that, on average, for 9.37% of all auto carriers, it is possible to load an additional vehicle compared with the current industry solution. This translates to 1.36% less total costs. The performance of the nesting approach is slightly worse, but as it turns out, it is well-suited to check load combinations for feasibility.

Solving Redundancy Allocation Problems using Jaya Algorithm

Reliability-based design is related to the performance analysis of engineering systems. The redundancy allocation problem is one of the most common problems in the reliability-based design approach. The redundancy allocation problem determines the redundancy level of components in a system to maximize system reliability, subject to several constraints. In recent years, obtaining solutions to reliability-related redundancy allocation problems by means of evolving meta-heuristic algorithms has appealed to researchers due to the several drawbacks of classical mathematical methods. Meta-heuristics have shown the potential of obtaining precise solution in optimization problems and many techniques have been applied in the literature for optimal redundancy allocation. In this paper, a recently developed Jaya optimization algorithm is proposed to be applied for redundancy allocation to maximize system reliability. The Jaya algorithm is a simple, population-based intelligent meta-heuristic algorithm consisting of a single phase and an algorithm-specific parameter-less algorithm. This paper aims to present an application of the Jaya algorithm for searching the optimal solution of two redundancy allocation problems from the literature with nonlinear constraints so that system reliability is maximized. The first problem is the over speed protection system for a gas turbine, whose control system is modelled as a four-stage series system. The objective is to determine the optimal level of redundancy of the valves of the protection system under cost and weight constraints. The second one is the redundancy allocation problem of a five-stage series system with volume, weight, and cost constraints. The results are validated by comparing them with two other meta-heuristics.

Hybrid ML-EMT-Based Digital Twin for Device-Level HIL Real-Time Emulation of Ship-Board Microgrid on FPGA

Maritime industries desire high speed and reliability, low lifespan cost, and environmental impact shipping for transportation. Compared to highly congested land shipments and high-cost air freight, all-electric ship (AES) can reduce the lifespan energy consumption and transport a considerable freight volume at a lower rate. Recently, the medium voltage DC (MVDC) topology, recommended by IEEE standard, pushes the AES to the next stage in considering space and weight constraints with the reduction of bulky transformers and simplified parallel connections. However, device-level modeling of this massive parallel MVDC-based ship-board microgrid (SBM) is challenging to both the state-of-the-art general-purpose compute unit and traditional electromagnetic transient (EMT) based emulation. With the rapid development of machine learning (ML) algorithm and its dedicated execution unit, accelerated parallel emulation becomes achievable in different levels of this paralleled connected SBM. Applying the ML-aided technique can help to improve the emulation execution efficiency and reduce the consumption of hardware resource on the field-programmable gate arrays (FPGAs). This work proposes a real-time hybrid ML-EMT based digital twin of the complete SBM at the subsystem-level and equipment-level with validated results from PSCAD/EMTDC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> , and device-level with validated results from SaberRD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> .

Fixed charge 4-dimensional transportation problem for breakable incompatible items with type-2 fuzzy random parameters under volume constraint

Real-life problems are full of uncertainties combined with impreciseness and randomness. Transportation problems become difficult when its parameters are type-2 fuzzy random type. This paper presents a 4-dimensional fixed charge transportation problem for incompatible and compatible breakable items with type-2 fuzzy random parameters under the availability, demand, conveyance’s weight and volume capacity constraint. The model is developed for usual transportation cost minimization objective, where transportation costs, fixed charges, availabilities, demands, conveyances’ weights and volume capacities are type-2 fuzzy random. A type-2 fuzzy random parameter is converted to type-1 fuzzy random one by critical value-based reduction method and then converted to a random parameter using two different methods-generalized credibility measure and centroid method. The random parameter is converted to crisp parameter using probability chance constraint method for constraints and expectation for objective. A detailed conversion procedure from type-2 fuzzy random to crisp is also presented. The reduced deterministic problem is solved by the Generalized Reduced Gradient method using Lingo 11.0 and numerically illustrated. A real-life example is introduced. Particular models are deduced for non-breakable items or different pairwise incompatible items. The sensitivity analyses against different generalized credibility levels of different parameters are presented for total transportation cost. The numerical results show the efficiency of the proposed critical value reduction-generalized credibility-chance constraint, and expectation method. The importance of uncertainty, multi-path and vehicles’ volume capacity in transportation problems is demonstrated.

MULTILEVEL INVERTER WITH REDUCED SWITCHES FOR ELECTRICAL VEHICLE

This project focuses on the development of an innovative multilevel inverter designed specifically for electric vehicles (EVs), aiming to reduce complexity and enhance efficiency by minimizing the number of switches in the inverter topology. The conventional multilevel inverter configurations often involve a high count of switches, leading to increased costs, intricate designs, and elevated power losses. In contrast, our approach seeks to optimize the inverter's performance by employing advanced control strategies and modulation techniques while significantly reducing the number of switches. This reduction not only simplifies the design but also lowers production costs and maintenance efforts. Moreover, the project emphasizes the compact integration of the inverter into the EV powertrain, addressing the size and weight constraints of electric vehicles. Through thorough simulations and practical experiments, the proposed multilevel inverter's performance will be evaluated in terms of efficiency, power quality, and reliability. The anticipated outcomes aim to contribute to the advancement of power electronics for sustainable transportation, providing a more efficient, cost- effective, and compact solution tailored to the unique requirements of electric mobility. This research has the potential to significantly impact the field, fostering further innovations in power electronics for clean and efficient transportation solutions .Key Words: Electric vehicle, Inverters, Switches, Efficiency.

Advancements in Spaceborne Synthetic Aperture Radar Imaging with System-on-Chip Architecture and System Fault-Tolerant Technology

With the continuous development of satellite payload and system-on-chip (SoC) technology, spaceborne real-time synthetic aperture radar (SAR) imaging systems play a crucial role in various defense and civilian domains, including Earth remote sensing, military reconnaissance, disaster mitigation, and resource exploration. However, designing high-performance and high-reliability SAR imaging systems that operate in harsh environmental conditions while adhering to strict size, weight, and power consumption constraints remains a significant challenge. In this paper, we introduce a spaceborne SAR imaging chip based on a SoC architecture with system fault-tolerant technology. The fault-tolerant SAR SoC architecture has a CPU, interface subsystem, memory subsystem, data transit subsystem, and data processing subsystem. The data processing subsystem, which includes fast Fourier transform (FFT) modules, coordinated rotation digital computer (CORDIC) modules (for phase factor calculation), and complex multiplication modules, is the most critical component and can achieve various modes of SAR imaging. Through analyzing the computational requirements of various modes of SAR, we found that FFT accounted for over 50% of the total computational workload in SAR imaging processing, while the CORDIC modules for phase factor generation accounted for around 30%. Therefore, ensuring the fault tolerance of these two modules is crucial. To address this issue, we propose a word-length optimization redundancy (WLOR) method to make the fixed-point pipelined FFT processors in FFT modules fault tolerant. Additionally, we propose a fault-tolerant pipeline CORDIC architecture utilizing error correction code (ECC) and sum of squares (SOS) check. For other parts of the SoC architecture, we propose a generic partial triple modular redundancy (TMR) hardening method based on the HITS algorithm to improve fault tolerance. Finally, we developed a fully automated FPGA-based fault injection platform to test the design’s effectiveness by injecting errors at arbitrary locations. The simulation results demonstrate that the proposed methods significantly improved the chip’s fault tolerance, making the SAR imaging chip safer and more reliable. We also implemented a prototype measurement system with a chip-included board and demonstrated the proposed design’s performance on the Chinese Gaofen-3 strip-map continuous imaging system. The chip requires 9.2 s, 50.6 s, and 7.4 s for a strip-map with 16,384 × 16,384 granularity, multi-channel strip-map with 65,536 × 8192 granularity, and multi-channel scan mode with 32,768 × 4096 granularity, respectively, and the system hardware consumes 6.9 W of power to process the SAR raw data.

Joint message passing decoding for LDPC codes with CCDMs.

In this Letter, message passing algorithms are introduced to jointly decode low-density parity-check (LDPC) codes and constant composition distribution matchers (CCDMs) to achieve better performance. Then, based on the weight constraint of CCDMs, a weight decoding with linear complexity is proposed to efficiently decode the CCDM codewords. Simulations over a fiber channel show that short CCDMs with the proposed joint decoding provide up to 10.6% reach increase compared with long CCDMs with one-pass decoding.

A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones

Innovative drone-based technologies provide novel techniques to guarantee the safety and quality of power supply and to perform these tasks more efficiently. Electric multirotor drones, which are at the forefront of technology, face significant flight time limitations due to battery capacity and weight constraints that limit their autonomous operation. This paper presents a novel drone charging station that harvests energy from the magnetic field present in power lines to charge the drone’s battery. This approach relies on a charging station that is easy to install by the drone on an overhead AC power line without modifying the electrical infrastructure. This paper analyses the inductive coupling between the energy harvester and the power line, electrical protection, the power electronics required for maximum power point tracking and the mechanical design of the charging station. A drone that perches on a cable, an end effector for installation procedures and the charging maneuver are described, along with discussion of the robotic and electrical tests performed in a relevant environment. Finally, a lightweight drone charging station capable of harvesting 145 W of power from a 600 A line current is reported.

Time and phase features network model for automatic modulation classification

Automatic Modulation Classification (AMC) constitutes a fundamental technology for enabling automatic demodulation in Cognitive Communication Systems (CCS). Due to the size, weight, and power (SWaP) constraints of embedded computers employed in CCS, there are limited computational and memory resources. While deep neural networks possess strong feature representation and high accuracy recognition capabilities, they usually come with a high number of network parameters and high computational complexity, thereby reducing the real-time processing ability of CCS. Therefore, neural network structures intended for CCS must be lightweight and computationally efficient. In this paper, we propose a high-performance and resource-friendly network model based on an analysis of the modulation mechanism of communication signals. The network extracts phase features and short-time features sequentially using directional convolutional filters. Long short-term memory (LSTM) units are then used to extract long-term features, and only one fully connected layer is used for classification. Experiments with a standard dataset consisting of 11 communication modulation types demonstrate that our proposed model achieves an accuracy greater than 84.5%, even when the signal-to-noise ratio (SNR) is 0 dB, and the model has only 29187 parameters. On a Jetson Nano embedded platform, the model achieves a processing speed of up to 375366 in-phase and quadrature samples/s. Overall, the results suggest that our proposed approach is both lightweight and highly efficient, making it more suitable for CCS applications.

Design and Fabrication of a Thin and Micro-Optical Sensor for Rapid Prototyping.

Optical sensing offers several advantages owing to its non-invasiveness and high sensitivity. The miniaturization of optical sensors will mitigate spatial and weight constraints, expanding their applications and extending the principal advantages of optical sensing to different fields, such as healthcare, Internet of Things, artificial intelligence, and other aspects of society. In this study, we present the development of a miniature optical sensor for monitoring thrombi in extracorporeal membrane oxygenation (ECMO). The sensor, based on a complementary metal-oxide semiconductor integrated circuit (CMOS-IC), also serves as a photodiode, amplifier, and light-emitting diode (LED)-mounting substrate. It is sized 3.8 × 4.8 × 0.75 mm3 and provides reflectance spectroscopy at three wavelengths. Based on semiconductor and microelectromechanical system (MEMS) processes, the design of the sensor achieves ultra-compact millimeter size, customizability, prototyping, and scalability for mass production, facilitating the development of miniature optical sensors for a variety of applications.

Photoelectrochemical Devices for Oxygen and Fuel Production on Moon and Mars

Human space exploration and the realisation of a green energy economy on Earth are confronted with the same challenge: both require efficient, stable and reliable renewable energy systems.1 Currently existing solutions such as the water electrolyzer in operation on the International Space Station (ISS) for oxygen and hydrogen production are not feasible in future lunar and Martian habitats as they are too bulky and heavy to transport and they lack the required reliability and efficiency2. Photoelectrochemical (PEC) devices not only meet the weight and volume constraints imposed onto devices for space applications by utilising a monolithic design for light absorption, charge separation and catalysis,3 they can also directly be coupled to solar concentrators in order to harvest the abundant solar energy available in space. Despite the advantages, PEC devices are nearly exclusively researched and developed for terrestrial applications and only a very few examples explore the possibility of utilizing the integrated semiconductor-electrocatalyst systems for space applications.4 Here, we present an unprecedented theoretical analysis of the applicability of PEC devices for lunar solar water-splitting and Martian CO2 reduction. These target reactions are in line with roadmaps of international space organisations aiming at a habitat establishment on the lunar South Pole, where water ice is assumed to be present in the Shackleton Crater. Furthermore, the atmosphere on Mars is composed of about 96% gaseous CO2, motivating our choice to investigate Martian CO2 to CO and CH4 conversion.5 Both reaction products could be used as propulsion additives or as precursors for the synthesis of hydrocarbon-based molecules in analogy to terrestrial applications. Firstly, we calculated a refined Martian solar irradiance spectrum and established the theoretical framework to describe PEC devices on Moon and Mars before calculating device limiting efficiencies and expected product rates for realistic devices at different solar irradiance scenarios. Our findings emphasise that PEC devices are indeed applicable on Moon and Mars, particularly when coupled to solar concentrators. In addition, it is evident that gas diffusion electrodes (GDEs), which mark a significant part of CO2 reduction devices investigated terrestrially, are surprisingly not necessarily viable in situations of limited solar irradiance such as present on Mars.References Ross B, Haussener S, Brinkert K. Photoelectrochemical devices for oxygen and fuel production on Moon and Mars. In review. Brinkert K, Mandin P. Fundamentals and future applications of electrochemical energy conversion in space. npj Microgravity 8, (2022).Cheng W-H, et al. Monolithic Photoelectrochemical Device for Direct Water Splitting with 19% Efficiency. ACS Energy Letters 3, 1795-1800 (2018). Brinkert K, et al. Efficient solar hydrogen generation in microgravity environment. Nature Communications 9, (2018). Yoshizaki T, McDonough WF. The composition of Mars. Geochimica et Cosmochimica Acta 273, 137-162 (2020).

Hydrogen Gas Bubble Evolution on Electrocatalytic Nanostructures in Microgravity Environment

Photoelectrochemical (PEC) monolithic devices for oxygen and chemical production are attractive for space applications due to predominant weight and volume constraints as well as a high system tunability. Here, advanced semiconductor-electrocatalyst systems integrate the processes of light absorption, charge separation and catalysis in one device - processes, which are usually carried out e.g., on the International Space Station (ISS) by an electrolyser coupled to external PV cells. Due to the near absence of buoyancy and buoyancy-induced convection in micro gravity, all (photo)electrolyser systems in space naturally have lower efficiencies, as gas bubble desorption and phase separation are severely hindered. We report on a new approach to design the electrocatalyst morphology on a nanoscale in order to modify the physicochemical properties of the photoelectrode and thus allow for continuous gas bubbles desorption and efficient operation of the PEC device in microgravity. Using a rhodium-coated p-InP model photoelectrochemical system, we discuss different Rh electrocatalyst nanotopographies fabricated by shadow nanosphere lithography and report on their characterisation as well as performance in photoelectrochemical measurements realised during 9.2 s of free fall at the Bremen Drop Tower, Center of Applied Space Technology and Microgravity (ZARM). In particular, we elucidate on how the electrocatalyst nanostructures affect the gas bubble size during growth and at detachment from the electrode and conclude on an efficient (photo-)electrode design for hydrogen evolution in microgravity.