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At least 37 records · Page 2

Tackling the Giants: Applying Smart Labs Principles to Constant Air Volume Lab Buildings

Laboratories typically consume 3 to 10 times more energy than similarly sized commercial buildings, and as much as 50% of that energy is wasted by inefficient and poorly operating fume hoods and ventilation systems. One challenge faced by older laboratory buildings is the heating, ventilation, and air-conditioning systems serving many of these buildings. The older systems are usually constant air volume (CAV) systems that maintain constant ventilation rates that cause excess airflow and inefficient energy use. Variable air volume systems can be more efficient systems with sensors to detect the need for a change in volumetric flow rate; however; renovation of ventilation systems can create disruption to ongoing research and operations along with considerable up-front costs. When a Smart Labs program is implemented, an organization has a systems-based management approach that yields a high-performing laboratory building. As decarbonization continues as a priority for sites, buildings with CAV systems are difficult to address. This work centers around practical guidance for improving lab buildings with CAV. In conjunction with industry input on top technology solutions and best practices, recommendations will include performing a laboratory ventilation risk assessment in conjunction with robust retro-commissioning work, which is a crucial step in the Smart Lab process. By applying Smart Labs principles, the aging laboratory building stock of 153,343 (Lawrence Berkeley National Laboratory [LBNL] 2017), comprising roughly 500,000 lab spaces in the United States, can be brought to safe and high-performance operations.

building

Smart manufacturing approach to manufacture bulk nanocrystalline aluminum for lightweight applications

In this research, a smart manufacturing approach was used to enhance the mechanical properties of aluminum (Al) for lightweight applications. The smart manufacturing involved cryomilling of Al powders with and without 5 wt.% magnesium (Mg) powders for varying durations followed by a high-pressure cold spray (HPCS) additive manufacturing process to prepare bulk components. The morphological changes, crystallite size, and composition of the cryomilled powders and cold sprayed (CS’ed) components were examined using scanning electron microscopy (SEM), x-ray diffraction (XRD), and transmission electron microscopy (TEM) techniques. The results showed that the crystallite size reduces with an increase in cryomilling time and the addition of Mg dopant. To test the mechanical properties of the bulk CS’ed components, microhardness tests were performed using a Vickers microhardness tester. Uniaxial tensile tests were also carried out to ascertain the material’s tensile properties. The mechanical testing results showed great improvement in the hardness and tensile strength of CS’ed Al–Mg samples as compared to pure Al samples. Subsequently, fractography analysis of the tensile failed samples was carried out to determine the nature of the failure. Here, the research article also discusses the inherent mechanisms for the improvement in mechanical properties of smart manufactured components as a result of Mg doping and cryomilling.

36 MATERIALS SCIENCE

Smart culture medium optimization for recombinant protein production: Experimental, modeling, and AI/ML-driven strategies

Recombinant protein production (RPP) is central to biotechnology, where recombinant proteins are used as either end products or catalysts in the synthesis of chemicals, fuels, and materials. Among the major cost drivers, culture medium plays a pivotal role in determining protein yield and quality. This review presents a comprehensive perspective on the critical stages of “smart” culture medium optimization: planning, screening, modeling, optimization, and validation. In the planning stage, we examine the nutritional and energetic roles of medium components, including carbon, nitrogen, amino acids, salts, and trace metals, and their impacts on culture parameters such as pH, oxidative state, and osmolality. We highlight the variability in trace metal content due to water sources, culture vessels, and raw materials, which can substantially influence RPP. The screening stage covers Design of Experiments (DoE) approaches, assessing their theoretical basis, implementation, and limitations. For modeling, we describe methods that integrate experimental data to develop predictive models for smart medium formulation. Model-based optimization strategies can then be employed to select optimal media compositions for a given application. The validation stage aims to evaluate model predictions and provide feedback for model training and refinement. Finally, we survey mechanistic and artificial intelligence/machine learning (AI/ML)-driven models as integrated, transformational tools for predictive modeling of bioprocess conditions, nutrient availability, cellular metabolism, and protein quality, with the goal of optimizing culture media to enhance protein yields while reducing costs and environmental impact. We conclude by addressing the challenges of translating laboratory-scale medium optimization to industrial-scale settings and exploring future AI/ML-driven approaches that may overcome current bottlenecks and accelerate medium design for RPP. Overall, this review provides a unified framework for advancing smart medium design in RPP.

Artificial Intelligence/Machine Learning (AI/ML)

IoT-based retrofit information diffusion in future smart communities

Community-scale building retrofits are not merely scaled-up versions of single-building retrofits. They involve complex challenges, such as reconciling individual interests with collective goals and managing the dynamic interplay between buildings through mechanisms like power grids and social connections. Internet of Things (IoT) connectivity holds the potential to leverage these interplays to balance individual and collective interests effectively in smart communities. One critical aspect of this interplay is information diffusion, which shapes how retrofit decisions spread among neighbors, influencing individual choices and ultimately impacting community-level retrofit outcomes. In other words, IoT-based smart devices automatically push tailored retrofit notifications to homeowners, which completely changes the format of information diffusion in the future. To investigate this influence by such information diffusion, the study used CityBES to simulate energy performance for different retrofits and applied an information diffusion model to analyze how decisions spread in a networked community of 192 buildings. The diffusion process was modeled on a weighted, directed network, capturing the dynamics of information flow and decision-making across 16 scenarios. Individual retrofit benefits were evaluated through payback years, while community-level retrofit outcomes were assessed using greenhouse gas (GHG) emission reductions. The results demonstrate that easier information diffusion among neighbors encourages households to prioritize retrofit measures that align with the majority’s optimal choices, even at the expense of individual financial benefits. In this case, such collective prioritization enhanced community-level retrofit performance, increasing GHG emission reductions by up to 29.4 %. However, this improvement came with trade-offs, as the average payback period for households extended by approximately 1.74 years. These findings highlight the potential of IoT-based information diffusion in future smart communities to coordinate individual interests with collective goals, ultimately accelerating community-level building retrofits.

Shu, Lei

Design of a Thomson scattering diagnostic for the SMall Aspect Ratio Tokamak (SMART)

Here, we describe the design of a Thomson scattering (TS) diagnostic to be used on the SMall Aspect Ratio Tokamak (SMART). SMART is a spherical tokamak being commissioned in Spain that aims to explore positive triangularity and negative triangularity plasma scenarios at a low aspect ratio. The SMART TS diagnostic is designed to operate at high spatial resolution, 6 mm scattering length in the low-field side and 9 mm in the high-field side regions, and a wide dynamic range, electron temperature from 1 eV to 1 keV and density from 5 x 10 18 m -3 to 1 x 10 20 m -3 ⁠, to resolve large gradients formed at the plasma edge and in the scrape-off layer (SOL) under different triangularities and low aspect ratios. A 2 J @1064 nm laser will be used that is capable of operating in the burst mode at 1, 2, and 4 kHz to investigate fast phenomena and at 30 Hz to study 1 s (or more) long discharges. The scattered light will be collected over an angular range of 60° – 120° from 28 spatial points in the midplane covering the entire plasma width and the outer midplane SOL. Each scattering signal will be spectrally resolved on five wavelength channels of a polychromator to obtain the electron temperature measurement. We will also present a method to monitor in situ laser alignment in the core during calibrations and plasma operations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY

Commissioning of the Power Supplies and Coils of the SMART Tokamak

The small aspect ratio tokamak (SMART) is a spherical tokamak (ST) that offers unique capabilities for studying the potential of negative triangularity. It has been designed, constructed, and is currently being operated by the Plasma Science Fusion Technology (PSFT) Laboratory at the University of Seville. SMART has a total of 21 coils, organized into seven independent circuits and driven by five modular power supplies (PS). The PS operation relies on switching converter technology based on IGBT and supercapacitors (SCs). The PS delivers predefined current waveforms to the copper coil system consisting of the central solenoid (CS), 12 toroidal field (TF) coils, three series pairs of poloidal field (PF) coils, and two independent PF coils. This study details the commissioning and validation of the PS toroidal and solenoid coils, as well as the assembly of coils in SMART. The maximum rated current and slope were measured, along with the series impedance of the coils, the output current ripple, and the noise levels. The internal parameters of the SCs were measured, and optimized current profiles were proposed to enhance overall performance. A comparison has been made between the theoretical values and the experimental results, providing insight into the performance of the PS and areas for improvement.

Power electronics

Deploying MARTe2 and MDSplus for Scalable Real-Time Control Systems: A Proof-of-Concept for the SMART Tokamak

The SMART tokamak is a spherical tokamak with flexible shaping that will need a modular and high-performance real-time control system. This work presents the implementation and validation of the MARTe2 framework at the Princeton Plasma Physics Laboratory (PPPL) as a testbed for supporting SMART (University of Seville, Spain). The system integrates MARTe2, MDSplus for data archiving, and user datagram protocol (UDP)-based communication with both synthetic and physical devices. A deterministic control loop was developed using custom control algorithms, showing the system’s ability to operate under strict real-time constraints. The resulting framework is scalable, maintainable, and adaptable to experimental tokamaks like SMART.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY

Radiation-Hard Smart-Pixel Detector ASIC ReadOut with Digital AI in 28nm

Detectors at future high energy colliders will face enormous technical challenges. Disentangling the unprecedented numbers of particles expected in each event will require highly granular silicon pixel detectors with billions of readout channels. With event rates as high as 40 MHz, these detectors will generate petabytes of data per second. To enable discovery within strict bandwidth and latency constraints, future trackers must be capable of fast, power efficient, and radiation hard data-reduction at the source. This effort is pursuing the co-design development of high-performance readout smart pixel ASICs for a future Phase III High Luminosity upgrade of the Large Hadron Collider. A 1.6mm2 ASIC prototype was designed by Fermilab in CMOS 28 nm bulk process and submitted for manufacturing in February 2024. It leverages the analog front-end pixel design of a previous prototype fabricated and tested in 2023, which achieved a simulated detection level of ~400e- with 30fF input capacitance. The ROIC consists of two matrices of 16×16 smart pixels, each 25×25 μm2 in size. Each smart pixel contains a charge-sensitive preamplifier with leakage current compensation and three auto-zero comparators for a 2-bit flash-type ADC. There is digital space for the integration of our fully combinatorial AI that performs momentum classification at the bunch crossing rate. The total power consumption is ∼6μW per pixel, which corresponds to ~1mW/cm2. The ASIC incorporates programmable front-end charge injection circuitry to generate pixel cluster charges during characterization. The cluster profile will be generated to duplicate hit characteristics of various momentum (pT). We will present early results from chip testing.

Parpillon, Benjamin

Preparing large area of thermochromic nanocomposite films for smart window application

The challenges posed by high building energy bill necessitate proactive implementation of energy-efficient strategies to minimize the uses of both electricity and heating gas and promote the use of free solar energy sources. "Smart" window films/glass, leveraging thermochromic vanadium dioxide (VO 2 ), offer an adaptive approach to harness solar energy, significantly reducing the thermal load of buildings. This is achieved by reflecting the infrared portion of the solar spectrum through a phase transition in the monoclinic M-phase (M) of VO 2 induced by heating. In this study, a scalable continuous flow synthesis process invented by Argonne was employed to explore a wide parameter space, targeting the controlled synthesis of monoclinic VO 2 (M) nanoparticles with well-controlled sizes and morphologies. Additionally, a doping strategy and surface modifications were utilized for high-throughput tuning of the metal-to-insulator transition temperature. Strategies to enhance the solar modulation properties of VO 2 nanoparticles in polymer films were investigated through (i) morphological transformation from spherical to nanorod structures, (ii) surface modification with low refractive index ligands, and (iii) incorporation of additional thermochromic materials for improved solar energy modulation and aesthetically appealing colors in smart films. Furthermore, the study outlines scaling-up synthesis methods for VO 2 nanoparticles in a continuous flow reactor using hydrazine monohydrate. This comprehensive investigation provides valuable insights into the upscaling synthesis and design of advanced VO 2 /polymer composite smart window films with enhanced functionality, solar energy modulation and visible light transmittance, driving the technology a step close for industrial application.

14 SOLAR ENERGY

Voltage stability smart meter for analyzing voltage data and controlling an electrical power source and/or an electric appliance

Systems and methods for voltage stability monitoring and active/reactive power support are disclosed herein. In some embodiments, a smart electric meter of an end user in a grid power system can measure the voltage supplied to the end user via the grid power system, and can analyze the voltage data to detect critical voltage characteristics. The critical voltage characteristics may indicate that a voltage collapse event is likely. The smart electric meter can further estimate a voltage stability margin based on the voltage data. If necessary, the smart electric meter can control an electrical power source and/or an electric appliance positioned at or near the end user to increase the voltage stability margin.

Min, Liang

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling

Smart Adaptive Structures for an Ocean Wave Energy Converter

Ocean wave energy converters face significant challenges including cost-effectiveness, minimizing maintenance requirements, and withstanding extreme conditions. However, by utilizing smart materials, these converters could overcome these challenges. Such energy harvesters could use dielectric elastomer generators to convert ocean wave energy into electricity through their dynamic straining. Conversely, by applying electricity to these generators, they become actuators - dielectric elastomer actuators - thereby enabling them to alter their stiffness and adapt to the ever-changing ocean energy environments. Such active adaptation could enhance the converter's ability to: reach resonance with ocean waves and protect itself from dangerous waves. This study utilizes numerical analyses through the COMSOL software framework to evaluate the potential energy that could be harvested by a conceptual ocean wave energy converter based upon dielectric elastomer generators/actuators. The converter is composed of an external hull (that is a hollow cylinder), an inertial mass (that is a hollow cylinder and concentric with the hull), and 'spokes' - made of dielectric elastomer generators/actuators - that connect the hull to the inertial mass. Results of the numerical analyses include those outcomes arising from the conceptual converter being simulated via a sinusoidal motion analogous to ocean waves. That motion, therefore, causes relative motion between the converter's hull and inertial mass thereby dynamically stretching the corresponding connecting elastomers. The stretching of the elastomers enables them to 'gain elastic strain energy' and is, therefore, considered to be the theoretical limit of possible electrical energy conversion for the dielectric elastomer generator/actuator spokes. Additionally, the elastomer material properties of the spokes were altered to simulate the actuation of those same elastomers; with overall strain energy being subsequently investigated. Ultimately this is a preliminary study exploring the ability of such smart materials - electricity-generating and self-actuating elastomers - to actively adapt an ocean wave energy converter's structure to address and overcome the aforementioned challenges.

active materials

Smart Energy Storage Integration and Management Platform for Buildings (SESIMP-B)

This project involved the design, development, and testing of a commercial smart service panel. It includes an innovative AI-driven control method to manage smart functions of the panel and a technical-economic feasibility assessment for enhancing building energy resilience and grid-edge technology integration.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI

Real-Time Testbed for Studying Cyberattacks and Defense in DER-integrated Smart Inverter Systems

In this paper, we propose a Hardware-in-the-Loop (HIL) simulation testbed suitable for the implementation and testing of realistic cyberattacks on grid-tied smart inverter systems integrated with Distributed Energy Resources (DER) that use the Distributed Network Protocol-3 (DNP3) protocol for communications between grid components. Specifically, our testbed combines a Real-Time Digital Simulator (RTDS) NovaCor device, outfitted with GNETx2 network interface cards, a gridtied DER topology implemented via the RTDS software package RSCAD, and a custom virtual network that emulates a man in the middle attacker. The Man-in-the-Middle (MITM) attacker captures DNP3 traffic and falsifies telemetry data in DNP3 packets to trigger unwarranted commands from a DNP3 controller that exploit smart inverter grid support functions. We choose DNP3 and implement grid support functions according to the IEEE Std. 1547-2018 mandated for the interconnection and interoperability of DER power systems with associated power components. Furthermore, we develop a protocol payload agnostic attack detection framework that leverages the round-trip time (RTT) anomalies between DNP3 requests and responses and can detect the presence of attacks without having to analyze the payload’s contents, while balancing trade-offs between false alarm counts, missed detections, and time to detection. To facilitate further research, we publicly release benign and attack network traffic exchanged between various sensors, controllers, and actuators in our grid-tied inverter testbed.

24 POWER TRANSMISSION AND DISTRIBUTION

Managing Workplace Charging: Argonne National Laboratory’s Reservation-Based Smart EV Charging Platform

The Smart Electric Power Alliance (SEPA) partnered with Argonne National Laboratory (Argonne) to produce a case study on Argonne’s workplace electric vehicle (EV) charging program, designed to optimize employees’ ability to reserve EV chargers and allow Argonne to implement a workplace managed charging solution. Formally known as EVrez, the program offers Argonne’s employees access to more than 50 Level 2 chargers and 4 DC fast chargers (DCFC). Employees must reserve and manage their EV sessions through the EVrez mobile app platform. This report outlines the EVrez program, from inception to maturity, highlighting key learnings and best practices from the Argonne team. As other workplaces seek to offer their own workplace charging offerings, this report highlights foundational steps and considerations.

24 POWER TRANSMISSION AND DISTRIBUTION

Smart Droplets Stabilized by Designer Surfactants: From Biomimicry to Active Motion to Materials Healing

The science and technologies of emulsion droplets have been a long‐term focus of extensive research endeavors for their practical utility across a breadth of industries, including pharmaceutical products, oil recovery processes, and the food sciences. However, with advances in materials chemistry and characterization tools, new emerging areas are arising with a focus on “smart droplets”. The versatility of emulsion droplets across is based on their ability to partition and create isolated systems with properties defined by the liquid–liquid interface, while preparative routes allow manipulation of droplet size, stability, and encapsulated contents. As described in this article, significant efforts are being devoted to creating new types of droplets by “activating” this interface through the incorporation of reactive structures that trigger droplet response to applied or environmental stimuli (e.g., pH, temperature, salt, or external fields). Moreover, parallels between droplets and live cells inspire efforts to conceive systems that resemble biological motifs or that can produce cellular behaviors that imitate biology (e.g., swarming, communication, or motion). Here, the authors highlight recent advances in smart droplets, with emphasis on organic, polymer, and/or particle surfactants that give rise to inter‐droplet communication (via aggregation, fusion, division, or mass transfer), droplet vehicles for controlled delivery, autonomous droplet motion, and tunable emulsion inversion. Especially emphasized is the macromolecular design to produce reactive and functional surfactants, which are crucial to responsive droplet behavior and their underlying mechanisms. More generally, the exquisite interplay between materials science and biology inspires the review of this research area that provides unique opportunities for insight and inspiration into the capabilities of new droplet designs.

36 MATERIALS SCIENCE

Dynamic Interfacial Design in Adaptive Hybrid Materials Enables Reversible and Tunable Mechano-Optic Smart Responses

Next-generation polymeric materials are shifting toward adaptive and interactive behaviors of living systems; however, designing materials that can reversibly modulate optical properties under mechanical deformation while maintaining mechanical robustness remains a key challenge. Here, we report a mechanically robust vitrimer-based adaptive hybrid material (AHM) that exhibits a stretch-induced reversible transparency-to-opacity transition, enabled by the integration of dynamic interactions at the polymer–silica nanoparticle interface and controlled nanoparticle self-assembly. The AHM combines boronic ester–functionalized polystyrene-b-poly(ethylene-co-butylene)-b-polystyrene (S-Bpin) with diol-functionalized silica nanoparticles (diol-SiNPs) to form a hybrid network hosting both dynamic boronic ester and hydrogen-bonding interactions. These reversible linkages facilitate controlled nanoparticle self-assembly and enable strain-induced nanoparticle alignment/aggregation. Upon stretching, SiNP-rich domains align and aggregate within the polymer matrix, while local modulus mismatch between stiff aggregated SiNP/borylated-styrene-rich regions and the softer elastomeric midblock induces surface microwrinkle formation. These internal aggregates and surface wrinkles cooperatively enhance light scattering, producing the opaque state under strain. Furthermore, the tailored AHM exhibits high toughness, thermomechanical stability, reprocessability, and programmable shape-memory behavior. This work presents a dynamic interfacial design strategy for mechanically robust, optically reconfigurable, and reusable soft materials for adaptive optics, smart windows, sensing, soft robotics, and circular smart-material platforms.

adaptive hybrid materials

Initial electron cyclotron heating/current drive scoping study for the SMall Aspect Ratio Tokamak (SMART)

In this work an initial electron cyclotron heating/current drive (EC H&CD) scoping study is presented for the SMall Aspect Ratio Tokamak (SMART), constructed and operated by the Plasma Science and Fusion Technology (PSFT) Laboratory of the University of Seville. We consider two planned phases of operation with different magnitude of magnetic field (B 0.4, and 1 T). This EC H&CD scoping study is carried out by using the ray tracing code TRAVIS, which allows to quickly perform a series of scans on the main SMART plasma scenarios. A scan in the poloidal and toroidal launching angles is performed for the B 0.4 T plasma scenario assuming three launching locations (mid-plane, off-mid-plane, and top-launcher) and the extraordinary (X-) mode polarization. The absorption occurs at the second harmonic and, overall, the highest power absorption is obtained launching the EC beam from the top of the machine. A numerical analysis of the fundamental X-mode EC H&CD start-up regime for the B 1.0 T plasma scenario with two different wave frequencies (28 and 35 GHz) is presented showing a very high current drive efficiency.

Bertelli, Nicola [Princeton Plasma Physics Laborat