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At least 649 records · Page 36

Optimizing Aerobot Exploration of Venus

Venus Flyer Robot (VFR) is an aerobot; an autonomous balloon probe designed for remote exploration of Earth's sister planet in 2003. VFR's simple navigation and control system permits travel to virtually any location on Venus, but it can survive for only a limited duration in the harsh Venusian environment. To help address this limitation, we develop: (1) a global circulation model that captures the most important characteristics of the Venusian atmosphere; (2) a simple aerobot model that captures thermal restrictions faced by VFR at Venus; and (3) one exact and two heuristic algorithms that, using abstractions (1) and (2), construct routes making the best use of VFR's limited lifetime. We demonstrate this modeling by planning several small example missions and a prototypical mission that explores numerous interesting sites recently documented in the plane tary geology literature.

SPACE EXPLORATION↗

Optimizing the regularization in size-consistent second-order Brillouin-Wigner perturbation theory

Despite its simplicity and relatively low computational cost, second-order Møller-Plesset perturbation theory (MP2) is well-known to overbind noncovalent interactions between polarizable monomers and some organometallic bonds. In such situations, the pairwise-additive correlation energy expression in MP2 is inadequate. Although energy-gap dependent amplitude regularization can substantially improve the accuracy of conventional MP2 in these regimes, the same regularization parameter worsens the accuracy for small molecule thermochemistry and density-dependent properties. Recently, we proposed a repartitioning of Brillouin-Wigner perturbation theory that is size-consistent to second order (BW-s2), and a free parameter ($α$) was set to recover the exact dissociation limit of H 2 in a minimal basis set. Alternatively $α$ can be viewed as a regularization parameter, where each value of $α$ represents a valid variant of BW-s2, which we denote as BW-s2($α$). In this work, we semi-empirically optimize $α$ for noncovalent interactions, thermochemistry, alkane conformational energies, electronic response properties, and transition metal datasets, leading to improvements in accuracy relative to the ab initio parameterization of BW-s2 and MP2. We demonstrate that the optimal $α$ parameter ($α$ = 4) is more transferable across chemical problems than energy-gap-dependent regularization parameters. This is attributable to the fact that the BW-s2($α$) regularization strength depends on all of the information encoded in the t amplitudes rather than just orbital energy differences. While the computational scaling of BW-s2($α$) is iterative $\mathcal{O}$($N^5$), this effective and transferable approach to amplitude regularization is a promising route to incorporate higher-order correlation effects at second-order cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Probing Ultrafast Charge Transfer Dynamics in Heterostructured Nanomaterials

The increasing demand for renewable and low‐cost energy has triggered a vast amount of research aimed at developing, characterizing and optimizing materials that can efficiently convert sun light into useful chemical energy in the form of electricity or chemical fuels. So‐ called quantum dot sensitized solar cell (QDSC) materials have gained much attention in recent years based on the advantageous physicochemical properties of semiconducting quantum dots (QDs) as sensitizers, as well as their promising potential to increase the conversion efficiency beyond the Shockley‐Queisser limit of ∼33% by making use of hot‐electron transfer (HET) routes and multiple exciton generation (MEG). By finely tuning the size of the QDs, the band gap can be carefully aligned to the metal oxide (MO) conduction band such that charge transfer from the photoexcited QD is thermodynamically favorable.

14 SOLAR ENERGY↗

A new high voltage alluaudite sodium battery insertion material

Large-scale stationary storage forms a key sector that can be economically served by sodium-ion batteries. In realizing practical sodium-ion batteries, discovery and development of novel cathodes is essential. In this spirit, alluaudite-type Na 2 Fe 2 (SO 4 ) 3 was reported in 2014 to have the highest Fe 3+ /Fe 2+ redox potential (~3.8 V vs. Na). This finding led to reports on various PO4 3– and SO4 2– based alluaudite compounds exhibiting high energy densities. In 2017, MoO 4 2– based alluaudite, Na 2.67 Mn 1.67 (MoO 4 ) 3 , was found as a 3.45 V cathode material. Exploring molybdenum chemistry further, this work reports alluaudite type Na 3.36 Co 1.32 (MoO 4 ) 3 (NCMo) as a novel versatile electroactive cathode for Li-ion and Na-ion batteries. It was synthesized by a wet solution-combustion route with a restricted annealing duration of 1 min at 600 °C. Calorimetric study revealed the formation enthalpy from component oxides (ΔH° f,ox = –575.49 ± 7.75 kJ/mol) to be highly exothermic. Unlike the sulfate class of alluaudites, this material is highly stable in air and moisture (ΔH ds = 537.42 ± 0.78 kJ/mol). Having an ionic conductivity of 6.065 × 10 –8 S/cm (at 50 °C), it offers a pseudo two-dimensional Na + migration pathway. Without any material optimization, NCMo was found to work as a high-voltage insertion cathode (ca. 4.0 V vs. Na/Na + and 4.1 V vs. Li/Li + ) in sync with theoretically predicted potential of 3.98 V (vs. Na/Na + ). Ex-situ X-ray diffraction and photoelectron spectroscopy studies revealed the occurrence of solid-solution redox mechanism solely involving Co 3+ /Co 2+ redox centre. Finally, it benchmarks Na 3.36 Co 1.32 (MoO 4 ) 3 as a novel electrochemically active Mo-based alluaudite-type polyanionic cathode insertion material.

25 ENERGY STORAGE↗

Landscape rules predict optimal superhighways for the first peopling of Sahul

Archaeological data and demographic modelling suggest that the peopling of Sahul required substantial populations, occurred rapidly within a few thousand years and encompassed environments ranging from hyper-arid deserts to temperate uplands and tropical rainforests. How this migration occurred and how humans responded to the physical environments they encountered have, however, remained largely speculative. By constructing a high-resolution digital elevation model for Sahul and coupling it with fine-scale viewshed analysis of landscape prominence, least-cost pedestrian travel modelling and high-performance computing, here we create over 125 billion potential migratory pathways, whereby the most parsimonious routes traversed emerge. Our analysis revealed several major pathways—superhighways—transecting the continent, that we evaluated using archaeological data. These results suggest that the earliest Australian ancestors adopted a set of fundamental rules shaped by physiological capacity, attraction to visually prominent landscape features and freshwater distribution to maximize survival, even without previous experience of the landscapes they encountered.

54 ENVIRONMENTAL SCIENCES↗

Natural Language Processing Analysis of Notices to Airmen for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized.

Natural Language Processing↗

Natural Language Processing (NLP) Analysis of NOTAMs for Air Traffic Management Optimization

With new emerging technologies in the field of NLP, we explore their applications to digitize and analyze heritage Air Traffic Management (ATM) documents for planning and optimizing airspace operations. Specifically, this research focuses on harvesting semi-structured or un-structured information contained in Notices to Airmen (NOTAMs). Using NLP and other advanced data analytics, we will construct a data-driven framework which facilitates finding language patterns and the use of pretrained language models for classification and extraction of useful airspace constraints and restrictions. These may lead to tools that assist airspace users in understanding the constraints more efficiently, contributing to better route planning and safer execution. This paper explores three workflows entailing different NLP tasks. First, unsupervised techniques like word embedding and topic modeling are used for pattern finding and document classification. Second, a dataset is created by extracting information from the semi-structured NOTAM format as metadata for categorizing, visualizing, and extracting key entities driving NOTAM content. Third, modern pre-built deep learning based transformer models such as BERT, RoBERTa, and XLNet are evaluated on the question answering task, an even more robust approach to information extraction, as well as their respective fine-tuning tasks. In this work we include various performance metrics for the trained models to evaluate both accuracy and precision and we show that the models can be generalized for their respective tasks. The research work developed shows promise in uncovering trends in digital NOTAMs in the NAS and also offers a new framework for digitizing and inferring insights from free-form legacy NOTAMs, that are yet to be digitized. Video is an mp4 download, with a play time of 9 min 35 secs.

Natural Language Processing↗

Optimal Pathways from Alternative Carbon Feedstocks to Organic Commodity Chemicals

The use of biogenic and waste feedstocks is a promising strategy to improve the chemical sector's supply chain resiliency and carbon intensity. To help inform research efforts that transform these feedstocks into industrial chemicals, we used a systematic analysis framework to consistently evaluate the economics and environmental impacts of >200 alternative production pathways for 51 organic commodity chemicals in the United States under an optimistic future scenario that reflects the potential upper bounds of process scalability, energy availability, and carbon uptake. Lower-impact and lower-cost alternative pathways were identified for all but three chemicals, with 75% using thermochemical routes and half leveraging existing manufacturing infrastructure. Scenario analysis shows that the ranking of these pathways for half of the assessed chemicals is particularly sensitive to carbon uptake assumptions and criteria prioritization (i.e., cost only, environmental impact only, or both), with changes in electricity grid mix, hydrogen source, and underlying mass and energy flow data proving less influential. Implementing alternative pathways for just 11 chemicals could support a transition to net-zero greenhouse gas emissions from chemical production by 2050, with 11% lower cost than business as usual, similar water requirements, quadrupled electricity demand, and the use of most available woody biomass. These findings provide an exploratory guide toward a future chemical industry that harnesses alternative feedstocks.

09 BIOMASS FUELS↗

Oxygen vacancy-rich amorphous FeNi hydroxide nanoclusters as an efficient electrocatalyst for water oxidation

Here, a one-pot strategy is presented to directly synthesize amorphous Fe x Ni y hydroxide nanoclusters (denoted as ANC-Fe x Ni y , <2 nm) with oxygen vacancies induced by ionic liquids. The ANC-Fe x Ni y catalyst presents abundant catalytic sites and high intrinsic conductivity. As such, the optimized ANC-Fe 1 Ni 2 exhibits high activity in oxygen evolution reaction (OER) with a Tafel slope of 39 mV dec –1 and an overpotential of 266 mV at 10 mA cm –2 . Notably, the optimized ANC-Fe 1 Ni 2 shows an extraordinarily large mass activity of 3028 A g FeNi –1 at the overpotential of 300 mV, which is ~24-fold of commercial RuO 2 catalyst. The superior activity of these Fe x Ni y hydroxide nanoclusters is ascribed to (i) the amorphous and distorted structure with abundant oxygen vacancies, and (ii) enhanced active site density by downsizing the ANC-Fe x Ni y clusters. This strategy provides a novel route for enhancing OER electrocatalytic performance and highly encouraging for the future application of amorphous metal hydroxides in catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantum Ratcheted Photophysics in Energy Transport

In this paper, we explore the scope of vibrations as quantum ratchets that serve as nonthermal routes to achieving population transport in systems where excitation transport between molecules is otherwise energetically unfavorable. In addition to their role as channels of transport, we investigate the effect of resonance of the vibrations, which are described by Huang-Rhys mixing, with excitonic energy gaps, which leads to strongly mixed vibronic excitons. Finally, we explore the interplay of resonance and Huang-Rhys mixing with electronic coupling between the molecules, in the presence of a dissipative bath, in optimizing transport in such systems. Furthermore, we find that while resonance is desirable, a moderate electronic coupling has a stronger positive effect in contrast to a large electronic coupling, which results in delocalized excitations across molecules and hampers unidirectional transport. We also report a special resonance regime that is able to circumvent the transport problems arising from large electronic couplings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Efficacy of commercial recombinant HVT vaccines against a North American clade 2.3.4.4b H5N1 highly pathogenic avian influenza virus in chickens

The outbreak of clade 2.3.4.4b H5 highly pathogenic avian influenza (HPAI) in North America that started in 2021 has increased interest in applying vaccination as a strategy to help control and prevent the disease in poultry. Two commercially available vaccines based on the recombinant herpes virus of turkeys (rHVT) vector were tested against a recent North American clade 2.3.4.4b H5 HPAI virus isolate: A/turkey/Indiana/22-003707-003/2022 H5N1 in specific pathogen free white leghorn (WL) chickens and commercial broiler chickens. One rHVT-H5 vaccine encodes a hemagglutinin (HA) gene designed by the computationally optimized broadly reactive antigen method (COBRA-HVT vaccine). The other encodes an HA gene of a clade 2.2 virus (2.2-HVT vaccine). There was 100% survival of both chicken types COBRA-HVT vaccinated groups and in the 2.2-HVT vaccinated groups there was 94.8% and 90% survival of the WL and broilers respectively. Compared to the 2.2-HVT vaccinated groups, WL in the COBRA-HVT vaccinated group shed significantly lower mean viral titers by the cloacal route and broilers shed significantly lower titers by the oropharyngeal route than broilers. Virus titers detected in oral and cloacal swabs were otherwise similar among both vaccine groups and chicken types. To assess antibody-based tests to identify birds that have been infected after vaccination (DIVA-VI), sera collected after the challenge were tested with enzyme-linked lectin assay-neuraminidase inhibition (ELLA-NI) for N1 neuraminidase antibody detection and by commercial ELISA for detection of antibodies to the NP protein. As early as 7 days post challenge (DPC) 100% of the chickens were positive by ELLA-NI. ELISA was less sensitive with a maximum of 75% positive at 10DPC in broilers vaccinated with 2.2-HVT. Both vaccines provided protection from challenge to both types of chickens and ELLA-NI was sensitive at identifying antibodies to the challenge virus therefore should be evaluated further for DIVA-VI.

60 APPLIED LIFE SCIENCES↗

Design and simulation of a descent controller for strategic four-dimensional aircraft navigation

A time-controlled navigation system applicable to the descent phase of flight for airline transport aircraft was developed and simulated. The design incorporates the linear discrete-time sampled-data version of the linearized continuous-time system describing the aircraft's aerodynamics. Using optimal linear quadratic control techniques, an optimal deterministic control regulator which is implementable on an airborne computer is designed. The navigation controller assists the pilot in complying with assigned times of arrival along a four-dimensional flight path in the presence of wind disturbances. The strategic air traffic control concept is also described, followed by the design of a strategic control descent path. A strategy for determining possible times of arrival at specified waypoints along the descent path and for generating the corresponding route-time profiles that are within the performance capabilities of the aircraft is presented. Using a mathematical model of the Boeing 707-320B aircraft along with a Boeing 707 cockpit simulator interfaced with an Adage AGT-30 digital computer, a real-time simulation of the complete aircraft aerodynamics was achieved. The strategic four-dimensional navigation controller for longitudinal dynamics was tested on the nonlinear aircraft model in the presence of 15, 30, and 45 knot head-winds. The results indicate that the controller preserved the desired accuracy and precision of a time-controlled aircraft navigation system.

Lax, F. M.↗

Designing a GIS-based supply chain for producing carinata-based sustainable aviation fuel in Georgia, USA

Carinata is a potential crop for sustainable aviation fuel (SAF) production in the southern USA. However, as a novel crop, the cost-effectiveness and environmental feasibility of carinata feedstock are unknown, and there are questions about the optimal supply chain configuration for carinata-based SAF production. This study aims to design a supply chain model for carinata-based SAF production by optimizing the location of farms and facilities (e.g. storage units, crushing mills, biorefineries) for a minimum transportation cost under a set of supply and demand conditions. An integrated mixed-integer linear programming (MILP) model was combined with geographical information system (GIS) analysis to design a spatially explicit supply chain configuration. The GIS-based network analysis considered all of the counties in Georgia to set the candidate locations of carinata farms and facilities, and determined minimum cost and emission routes between those counties and the airport using existing transportation networks and modes (e.g. road, rail and pipeline). The MILP model determined the final selection of the farms and the number of facilities and their locations over those minimum-cost routes. With this supply chain configuration, the minimum price of SAF was $\$$0.92 L –1 , which is $\$$0.44 higher than conventional aviation fuel (CAF). The associated carbon intensity of SAF was estimated at 940.7 g CO 2 e L –1 , a reduction of 66% relative to the carbon intensity of equivalent CAF. The study found that a carbon tax (or subsidy) of $\$$230.48 t CO 2 e –1 would be needed to overcome the cost differential with CAF and promote carinata-based SAF in Georgia.

09 BIOMASS FUELS↗

Pharmacotherapeutic Aspects of Space Medicine

Medications are used for a wide variety of indications during space flight. For example, astronauts have taken drugs in flight to ameliorate or prevent symptoms of space motion sickness, headache, sleeplessness, backache, nasal congestion, and constipation. Russian cosmonauts reportedly take medications to prevent metabolic disturbances of the myocardium and intestinal flora, and to optimize their work capacity. Although the discomfort associated with some acute responses to microgravity (e.g., space motion sickness) is expected to diminish with length of time in flight, other responses that have delayed onset (e.g., maintaining nutritional status, bone and muscle strength, and perhaps immune response) may affect health and quality of life during longer missions. Therefore, as the duration of space flights increases, the need for treatment with medications is expected to increase accordingly. Medications carried on Space Shuttle missions have varied somewhat from flight to flight, depending on the individual needs of the crewmembers. Medications use during Shuttle flights seems to be more prevalent than during earlier programs, perhaps because drugs are provided in easy-to-use forms. In fact, nearly all medications taken to date have been ingested orally in tablet form. However, given that the oral route may not be ideal for those suffering motion-sickness symptoms, intramuscular and intranasal preparations are being tested. For example, intramuscular administration of promethazine hydrochloride (Phenergan(Registered TradeMark)) has been reported to be more effective in alleviating motion-sickness symptoms. The difficulties involved in conducting definitive studies of drug efficacy during U.S. space flights have been compounded by the absence of a systematic approach to determining which drugs were taken by whom and under what circumstances. The use of some drugs in space has been less efficacious than expected. The onset, intensity, and duration of the response produced by any drug depend upon rates of absorption, distribution, metabolism, and elimination of the drug; space flight-induced changes in blood flow and the function of the gastrointestinal (GI) tract, liver, or kidneys may alter these processes. Another important aspect of clinical efficacy of medications in space is the stability of pharmaceuticals. As the U.S. space program is moving toward extended Space Shuttle flights and beyond, to space station missions and planetary explorations, understanding how space flight affects organ systems and clinical pharmacology is necessary to optimize pharmacotherapeutics in space and ensure adequate safety and health of crewmembers.

Putcha, Lakshmi↗

Ultra-light antennas via charge programmed deposition additive manufacturing

Abstract The demand for lightweight antennas in 5 G/6 G communication, wearables, and aerospace applications is rapidly growing. However, standard manufacturing techniques are limited in structural complexity and easy integration of multiple material classes. Here we introduce charge programmed multi-material additive manufacturing platform, offering unparalleled flexibility in antenna design and the capability for rapid printing of intricate antenna structures that are unprecedented or necessitate a series of fabrication routes. Demonstrating its potential, we present a transmitarray antenna composed of an interconnected, multi-layered array of dielectric/conductive S-ring unit cells, reducing 94% mass of conventional antenna configurations. A fully printed circular polarized transmitarray system fed by a source and a Risley prism antenna system operating at 19 GHz both show close alignment between testing results and numerical simulations. This printing method establishes a universal platform, propelling discovery of new antenna designs and enabling data-driven design and optimizations where rapid production of antenna designs is crucial.

Science & Technology - Other Topics↗

De novo design of pH-responsive self-assembling helical protein filaments

Abstract Biological evolution has led to precise and dynamic nanostructures that reconfigure in response to pH and other environmental conditions. However, designing micrometre-scale protein nanostructures that are environmentally responsive remains a challenge. Here we describe the de novo design of pH-responsive protein filaments built from subunits containing six or nine buried histidine residues that assemble into micrometre-scale, well-ordered fibres at neutral pH. The cryogenic electron microscopy structure of an optimized design is nearly identical to the computational design model for both the subunit internal geometry and the subunit packing into the fibre. Electron, fluorescent and atomic force microscopy characterization reveal a sharp and reversible transition from assembled to disassembled fibres over 0.3 pH units, and rapid fibre disassembly in less than 1 s following a drop in pH. The midpoint of the transition can be tuned by modulating buried histidine-containing hydrogen bond networks. Computational protein design thus provides a route to creating unbound nanomaterials that rapidly respond to small pH changes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Toward autonomous design and synthesis of novel inorganic materials

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve “closed-loop” materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Lastly, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.

36 MATERIALS SCIENCE↗

Synthesis of high-density olivine LiFePO 4 from paleozoic siderite FeCO 3 and its electrochemical performance in lithium batteries

The lithium-ion cathode material olivine LiFePO 4 (LFP) has been synthesized for the first time from natural paleozoic iron carbonate (FeCO 3 ). The ferrous carbonate starting material consists of the mineral siderite at about 92 wt. % purity. Because FeCO 3 has divalent iron, the reaction with lithium dihydrogen phosphate (LiH 2 PO 4 ) provides a unique method to develop iron-(II) containing LFP in an inert atmosphere. Since siderite FeCO 3 is a common mineral that can be directly mined, it may, therefore, provide an inexpensive route for the production of LFP. After carbon-coating, the LFP yields a capacity in the range of 80–110 mAh g -1 LFP (in one chosen specimen sample), which is lower than commercially available LiFePO 4 (150–160 mAh g -1 LFP ). However, the tap density of LFP derived from siderite is noticeably high at 1.65 g cm -3 . The material is likely to be improved with powder purification, nanosized processing, and more complete carbon-coating coverage with increased optimization.

36 MATERIALS SCIENCE↗