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At least 667 records · Page 37

Controlled Placement of Trivalent Heteroatoms in MFI Zeolite Frameworks Using Structure-Directing Agents

MFI zeolites contain distinct confining pore environments, either smaller (∼0.55 nm) channels or larger (∼0.70 nm) intersections. The substitution of trivalent heteroatoms generates Brønsted acid sites of varying acid strength, which, together with their location within different pore environments, dictates the stability of transition states via electrostatic (ion-pair) and van der Waals interactions. Here, we report that structure-directing agents (SDAs) that control Al 3+ substitutional patterns during MFI crystallization analogously position other trivalent heteroatoms (Ga 3+ , Fe 3+ , B 3+ ), altering the distribution of acid sites between channels and intersections, as probed by low-temperature (403 K) toluene methylation kinetics. Heteroatom-substituted MFI crystallized with tetrapropylammonium result in low selectivity toward p-xylene (<30%), while those crystallized using (co-)SDAs containing peripheral hydrogen-bonding groups (e.g., ethylenediamine) show high p-xylene selectivity (∼80%). These selectivities are consistent with DFT-calculated Gibbs free energy barriers for intersection-dominant and channel-dominant active site distributions, respectively. Transition states to form each xylene isomer are similar in size and charge; thus, their rate constants decrease with acid strength similarly, causing isomer selectivity to depend strongly on confinement but not on acid strength. These generalizable synthetic strategies enable independently controlling acid site strength and location in MFI zeolites and, in turn, catalytic rates and selectivities.

36 MATERIALS SCIENCE↗

Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation

Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. Here, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. In particular, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision-making process of a central manager, an agent-based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The findings indicate the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we discover that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade-off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward-based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.

54 ENVIRONMENTAL SCIENCES↗

High reactivity of mesoporous CeO 2 to dissociate chemical warfare agent sarin

We perform spectroscopic surface characterization on mesoporous ceria (CeO 2 ) under exposure of sarin gas, also known as GB. We find mesoporous CeO 2 displays extraordinary reactivity towards GB dissociation under high vacuum. The spectroscopic results combined with density functional theory (DFT) calculations suggest that the cleavage of the P–F and P–OR bonds are both possible pathways for GB to dissociate on the CeO 2 surfaces. The modeling reveals differences in dissociation pathways of GB on the crystallographic (111)- and (110)-surfaces of ceria. Here we also report some dissociation of GB on mesoporous CeO 2 under ambient conditions. The finding that mesoporous CeO 2 can degrade GB under mild conditions is encouraging and implies the strong potential of ceria-based materials to be applied in combating chemical warfare agents related to the GB series.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Green synthesis of gold nanoparticles using aqueous ethanol extract of Curcuma mangga rhizomes as reducing agent

Green synthesis of gold nanoparticles (AuNPs) had been developed as an alternative to chemical and physical methods due to its simplicity, cost effectiveness and eco-friendliness. The high biocompatibility and biostability features of AuNPs have found importance in biomedical applications in recent years. In this study, aqueous ethanol extract of Curcuma mangga rhizomes which acts as reducing and stabilizing agent was used to synthesize stable AuNPs by bioreduction of chloroauric acid. The formation of AuNPs was highlighted by the color change of the suspension from light yellow to reddish purple. Time-evolution was monitored by UV-visible spectroscopy, while surface plasmon (SP) absorption band of the AuNPs suspension was observed at a maximum absorption of 540 nm. Hydrodynamic radii and size distribution of the AuNPs in the suspension were evaluated using dynamic light scattering (DLS) and zeta potential measurement demonstrated negative surface charge. The particle size was calculated in the range of 2-30 nm using High Resolution Transmission Electron Microscopy (HRTEM). The morphology and elemental composition were further determined by Field Effect Scanning Electron Microscopy (FESEM) and Energy Dispersive X-ray (EDX) spectroscopy. Fourier transform infrared (FTIR) spectroscopy meanwhile was used to confirm the presence of AuNPs and functional groups involved in the gold bio-reduction process. Influence of the volume of extract and concentration of gold (III) chloride trihydrate (HAuCl{sub 4}.3H{sub 2}O) on the synthesis of AuNPs were also investigated. The results obtained indicate potential optimization and functionalization of AuNPs for future applications in bionanotechnology especially in the field of medicine.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Shape control of the magnetic iron oxide nanoparticles under different chain length of reducing agents

We report on the effect of using reducing agents with different chain-length on the synthesis of iron oxide nanoparticles by thermal decomposition of iron (III) acetylacetonate in 1-octadecene. This modification allows us to control the shape of nanoparticles into spherical and cubic iron oxide nanoparticles. The highly monodisperse 14 nm spherical nanoparticles are obtained under 1,2-dodecanediol and average 14 nm edge-length cubic iron oxide nanoparticles are obtained under 1,2-tetradecanediol. The structural characterization such as transmission electron microscope (TEM) and X-ray diffraction (XRD) shows similar properties between two particles with different shapes. The vibrating sample magnetometer (VSM) shows no significant difference between spherical and cubic nanoparticles, which are 36 emu/g and 37 emu/g respectively and superparamagnetic in nature.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Leader–follower fixed-time consensus of multi-agent systems with high-order integrator dynamics

The leader-follower fixed-time consensus of high-order multi-agent systems with external disturbances is investigated in this paper. A novel sliding manifold is designed to ensure that the tracking errors converge to zero in a fixed-time during the sliding motion. Then, a distributed control law is designed based on Lyapunov technique to drive the system states to the sliding manifold in finite-time independent of initial conditions. Finally, the efficiency of the proposed method is illustrated by numerical simulations.

42 ENGINEERING↗

Non-solvents as physical blowing agents in printable silicone foams

Silicone foams were produced by dispersing an incompatible liquid phase (i.e. a non-solvent) into an uncured, liquid silicone. The formulation and processing parameters were varied to see the effect on porosity and pore morphology. Specifically, two fluorosilanes were added to stabilize the inclusion of fluorinated solvents as blowing agents. As the floruosilane content increased, the void content increased up to about 44 vol. % when the fluorosilane comprised 18 wt. % of the initial formulation. Fumed silica that was treated with a fluorinated silane was also used to try to stabilize the dispersed liquid. While the fumed silica content did not have a strong effect on the total void content, the morphology changed when silica content changed. Various fluorinated solvents with distinct chemical structures were used as the non-solvent and then removed after curing of the silicone. The interaction of the internal non-solvent phase and the silicone phase was expected to influence the porosity. These insights highlight how the manipulation of formulation and processing parameters, focusing on the inclusion of fluorosilanes and fluorinated solvents, contributes to the understanding of how incompatible liquid phases interact with silicone matrices to control porosity and pore morphology. Additionally, these interactions also influenced processability, leading to formulations that could be printed. Higher content of non-solvent inclusions could increase the yield stress and storage modulus of an uncured formulation, leading to the ability to tune intrastrand porosity while a 3D printed architecture could be modified to introduce porosity between the strands. Furthermore, in addition to fluorinated solvents, we also considered non-fluorinated solvents since these may be more industrially relevant in the future. Overall, this approach provides an alternative route to producing porous foams that can be 3D printed, which could be useful for applications like cushioning and protective gear.

physical blowing agent↗

Generative diffusion model surrogates for mechanistic agent-based biological models

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

97 MATHEMATICS AND COMPUTING↗

VOLTTRON™: An Agent Platform for Integrating Electric Vehicles and Smart Grid

The VOLTTRON™ platform provides a secure environment for the deployment of intelligent applications in the smart grid. VOLTTRON design is based on the needs of control applications running on small form factor devices, namely security and resource guarantees. Services such as resource discovery, secure agent mobility, and interacting with smart and legacy devices are provided by the platform to ease the development of control applications and accelerate their deployment. VOLTTRON platform has been demonstrated in several different domains that influenced and enhanced its capabilities. This paper will discuss the features of VOLTTRON and highlight its usage to coordinate electric vehicle charging with home energy usage

VOLTTRON, agents, ev↗

Agentic AI and the Cyber Arms Race

Here, in this article, we examine the implications for cyberwarfare and global politics as agentic artificial intelligence becomes more powerful and enables the broad proliferation of capabilities only available to the most well-resourced actors today.

Cybersecurity↗

Exploring Social Dynamics of Hard-Disk Drives Circularity with an Agent-Based Approach

By 2025, it is estimated that installed data storage in the U.S. will be 2.2 Zettabytes, generating about 50 million units of end-of-life hard-disk drives (HDDs) per year. The circular economy (CE) tackles waste issues by maximizing value retention in the economy, for instance, through reuse and recycling. However, the reuse of hard disk drives is hindered by the lack of trust organizations have toward other means of data removal than physically destroying HDDs. Here, an agent-based approach explores how organizations' decisions to adopt other data removal means affect HDDs' circularity. The model applies the theory of planned behavior to model the decisions of HDDs end-users. Results demonstrate that the attitude (which is affected by trust) of end-users toward data-wiping technologies acts as a barrier to reuse. Moreover, social pressure can play a significant role as organizations that adopt CE behaviors can set an example for others.

28 EE - Advanced Manufacturing Office (EE-5A)↗

A Review on Simulation Platforms for Agent-Based Modeling in Electrified Transportation

As the use of combustion engine vehicles plays a deciding role in global warming, we can observe a trend to replace them with electric vehicles (EV) driven by new environmentally conscious policies and increasing technological capabilities. With improvements in driving range and reduction in prices come new challenges that may hamper the progress towards complete battery driven transportation. A major challenge for the increasing EV adoption is the planning of extensions to existing infrastructure or the inclusion of new infrastructure components in the planning process. This demands increasingly complex planning tools that can simulate the interplay between different stakeholders in modern transportation scenarios such as EVs, charging stations, energy providers, and general transportation participants. Simulation platforms for agent-based modeling in transportation have been developed as effective interactive tools that allow planners to explore different trade-offs across different scenarios with the ability to simulate the impact of policy or infrastructure decisions on the different stakeholders in the simulation. This article surveys several of the major simulation platforms that include modern EV-based forms of transportation and allow the simulation of relevant infrastructure components alongside the well established transportation simulations. These tools allow researchers to analyze expected traffic flow, identify possible charging station locations based on area demand, predict electrical grid demand, and more. Here this survey intends to make it easier for researchers to identify and apply a simulation platform in the context of supporting the increasing electrification of the transportation sector, enabling more efficient simulation and planning capabilities in this domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Protection of autonomous microgrids using agent-based distributed communication

This study presents a real-time implementation of autonomous microgrid protection using agent-based distributed communication. Protection of an autonomous microgrid requires special considerations compared to large scale distribution net-works due to the presence of power converters and relatively low inertia. In this work, we introduce a practical overcurrent and a frequency selectivity method to overcome conventional limitations. The proposed overcurrent scheme defines a selectivity mechanism considering the remedial action scheme (RAS) of the microgrid after a fault instant based on feeder characteristics and the location of the intelligent electronic devices (IEDs). A synchrophasor-based online frequency selectivity approach is proposed to avoid pulse loading effects in low inertia microgrids. Experimental results are presented for verification of the pro-posed schemes using a laboratory based microgrid. The setup was composed of actual generation units and IEDs using IEC 61850 protocol. The experimental results were in excellent agreement with the proposed protection scheme.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Practical benzylation of N,N -substituted ethanolamines related to chemical warfare agents for analysis and detection by Electron Ionization Gas Chromatography-Mass Spectrometry

The benzylation of three, low-molecular weight N,N-disubstituted ethanolamines related to chemical warfare agents (CWAs) to furnish derivatives with improved Gas Chromatography-Mass Spectrometry (GC-MS) profiles is described. Due to their low molecular weight and polar nature, N,N-disubstituted ethanolamines are notoriously difficult to detect by routine GC-MS analyses during Organisation for the Prohibition of Chemical Weapons (OPCW) proficiency tests (PTs), particularly in scenarios when they are present at low levels (~1-10 ppm) amidst more abundant interferences. Our studies revealed that the optimal derivatization conditions involved the treatment of the ethanolamine with benzyl bromide in the presence of an inorganic base (e.g., Na 2 CO 3 ) in dichloromethane at 55 °C for 2 hours. This optimized set of conditions were then successfully applied to the derivatization of N,N-dimethylethanolamine, N,N-diethylethanolamine and N,N-diisopropylethanolamine present separately at 1 and 10 μg/mL concentrations in a glycerol-rich matrix sample featured in the 48 th OPCW PT. The benzylated derivatives of the three ethanolamines possessed retention times long enough to clear the massive glycerol-containing matrix interferences. In conclusion, the protocol herein is introduced as an alternative method for derivatization of these CWA and pharmaceutically important species and should find broad applicability in laboratories where their routine forensic analysis is carried out.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Agent-based modeling and simulation for the circular economy: Lessons learned and path forward

Circular economy aims at decoupling human activities from resource use and creating wealth. However, many have questioned the link between increased circularity and sustainability, resulting in several methodological approaches being developed to answer that question. This article analyzes and discusses the insights gained from applying agent-based modeling and simulation to study the techno-economic and social conditions promoting circularity and sustainability. This article analyzes the benefits and limitations of this technology and discusses future methodology developments within the circular economy context. Moreover, six limits of the circular economy concept are used to interpret insights from the literature: thermodynamic limits, system boundary limits, limits posed by the physical scale of the economy, limits posed by path dependencies and lock-in, limits of governance and management, and limits of social and cultural definitions. Promising research avenues are to use this methodology with machine learning, industrial ecology methods, and detailed geographic information.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Combining Agent Based Modeling and System Dynamics to Investigate the Circularity of Plastics

The United States currently produces about 1 million metric ton of ocean plastic pollution annually. One proposed solution to combat plastic waste is a circular economy (CE), which aims to transition from today's take-make-waste linear pattern of production and consumption to a system where the value of resources is maximized over time. Two key methods in industrial ecology are useful in assessing the viability of CE: (1) System Dynamics (SD) and (2) Agent Based Modeling (ABM). In prior work, the plastic life cycle was modeled with SD and ABM. The two models calculate recycling rates and costs in different ways, making it difficult to pinpoint necessary next steps. We integrate the ABM and SD models - linking the emergent patterns from micro-level human decisions to system level processes - which allows a more comprehensive understanding of feedbacks, costs, and environmental impacts. The integrated model is more accurate, and can be used to visualize recycling rates and human health and environmental impacts over time. The difference between the integrated and original SD model prompts a Sobol sensitivity analysis, which is used to understand which behavioral factors most affect plastic recycling patterns. We find that the habitual component is typically the most influential in promoting positive recycling behavior. Additionally, we utilize the combined model to understand and visualize how various behavioral intervention scenarios, like improved access to recycling programs and cart tagging, influence recycling patterns; these results can guide future policy-making.

agent-based modeling↗

Tissue scale agent-based simulation of premalignant progressions in Barrett’s esophagus

Barrett’s esophagus (BE) is a benign condition of the distal esophagus that initiates a multistage pathway to esophageal adenocarcinoma (EAC). Short of frequent intrusive (and costly) surveillance, effective screening for neoplasia in BE populations is yet to be established since progressors are rare and virtually undetectable without routine biopsies, which often sample only a small portion of the BE tissue. As a result, reliable estimation of the true prevalence of dysplasia in a BE population and evidence-based optimization of screening for at-risk individuals is challenging. Data-driven microsimulations, i.e., model-generated instances of disease history in a predefined virtual population, have found utility in the EAC screening literature as low-overhead alternatives to real-world hypothesis testing of optimal interventions for dysplasia. Despite the successes, computational limitations, paucity of knowledge and data on Barrett’s dysplasia, and the complexities of disease progression as a multiscale multiphysics process have hindered the treatment of disease progression in BE as a spatial process. Agent-based modeling of nucleation and proliferation processes in dysplasia warrants exploration in this context as an approximation that operates at a trade-off between computational tractability and precise representation of the composition and physics of the substrate (tissue). In this study, we describe spatially resolved simulations of premalignant progression toward EAC in a coarse-grained model of Barrett’s tissue that resolves the metaplastic tissue at a length scale of 0.42 mm (~3300 crypts/mm 2 ). Finally, the model is calibrated to reproduce historical high-grade dysplasia prevalence when model-generated patients are screened using the Seattle protocol.

59 BASIC BIOLOGICAL SCIENCES↗

Enabling Real-Time Communication in Multi-Agent Systems: A Graph Neural Network Based Approach

Global connectivity enables effective coordination in Multi-Agent Systems (MAS). Solving these connection problems under hardware constraints is an NP-hard non-Euclidean Degree Constrained Minimum Spanning Tree (DCMST) problem. Prior MAS controllers coordinate team movement for task completion and collision avoidance; some considering Line-of-Sight (LOS) maintenance but prioritizing flexibility over guarantees. Evolutionary Algorithms (EA) have been shown to find good solutions for DCMST, but their performance degrades with larger populations required to support a large MAS. We present a method based on edge graph attention networks, trained offline to reduce online computation times. Empirical comparisons with greedy polynomial-time solvers and EA show that our method leverages latent graph information to consistently find constraint-satisfying solutions in less time.

connectivity maintenance↗