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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records

Efficient structure-factor modeling for crystals with multiple components

Diffraction intensities from a crystallographic experiment include contributions from the entire unit cell of the crystal: the macromolecule, the solvent around it and eventually other compounds. These contributions cannot typically be well described by an atomic model alone, i.e . using point scatterers. Indeed, entities such as disordered (bulk) solvent, semi-ordered solvent ( e.g. lipid belts in membrane proteins, ligands, ion channels) and disordered polymer loops require other types of modeling than a collection of individual atoms. This results in the model structure factors containing multiple contributions. Most macromolecular applications assume two-component structure factors: one component arising from the atomic model and the second one describing the bulk solvent. A more accurate and detailed modeling of the disordered regions of the crystal will naturally require more than two components in the structure factors, which presents algorithmic and computational challenges. Here an efficient solution of this problem is proposed. All algorithms described in this work have been implemented in the computational crystallography toolbox ( CCTBX ) and are also available within Phenix software. These algorithms are rather general and do not use any assumptions about molecule type or size nor about those of its components.

59 BASIC BIOLOGICAL SCIENCES↗

Enhanced Hanford High-Fluoride Waste Glass Property Data Development: Phase 1

This study focused on investigating the effects of fluorine concentration on simulated high-level waste glass properties to eventually establish a fluorine limit (as a single-component or multiple-component constraint) for glass formulations for high-fluoride Hanford wastes. This is a first step to provide data to understand the impacts of changing flowsheets on the mission duration and extent. A test matrix of 20 high-fluoride glasses was generated, and the chemical compositions were measured. The following properties were measured and tested against current model predictions: crystal formation after centerline canister cooling, crystallinity as a function of temperature, density, viscosity, electrical conductivity, toxic leaching characteristics using the toxicity characteristic leach profile (TCLP), product consistency using the product consistency test (PCT), and SO 3 solubility. Overall, current models failed to adequately predict most of the properties, possibly due to differences in compositional space used to generate the models and the current test matrix. Additional work is needed to more accurately assess the impacts of high-fluoride wastes on Hanford processing, including additional data collection over a broader composition region and model development for the key models of interest such as PCT and TCLP.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Enhanced Hanford High-Aluminum Waste Glass Property Data Development

This study investigated the effects of aluminum concentration on simulated high level waste glass properties to eventually establish an aluminum limit (as single-component or multiple-component constraints) for glass formulations for high-aluminum Hanford wastes. A test matrix of 25 high-aluminum glasses (20 = Al 2 O 3 = 28.57 wt%) was generated, and the chemical compositions were measured. The following properties were measured: crystal formation after centerline canister cooling, crystallinity as a function of temperature, density, viscosity, electrical conductivity, toxic leaching characteristics using the Toxicity Characteristic Leaching Procedure, product consistency using the Product Consistency Test, and SO 3 solubility. These results are reported here.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Data for Spatial Analysis of Cell Patterning to Aid Genetic and Phenotypic Understanding of Grass Stomatal Density: A Case Study in Maize

Biological processes involve complex hierarchies where composite traits result from multiple component traits. However, holistically understanding of how sets of component traits interact to underpin genotype-to-phenotype relationships is generally lacking. Stomatal density (SD) is a tractable model system for exploring how high-throughput phenotyping (HTP) data could be exploited by a new spatial analysis approach to better understand a developmentally and functionally important trait. SD is a composite trait, resulting from various components related to cell identity and size, which are themselves governed by a series of spatio-developmental processes. Data from 192 recombinant inbred lines of maize [Zea mays (L.)] were analyzed by a new stomatal patterning phenotype (SPP) to (1) describe the average spatial probability distribution of the nearest neighboring stomata; (2) derive a core set of component traits related to cell size, cell packing, and positional probabilities; (3) build a structural equation model of component traits underlying SD; and (4) identify stomatal patterning quantitative trait loci (QTL). The core set of SPP-derived traits explained 74% of the variation in SD. Analyzing SPP component traits allowed some loci previously identified as generic SD QTL to be recognized as specific to lateral versus longitudinal elements of stomatal patterning. Therefore, this study highlights how novel insights can be gained by decomposing a composite trait (e.g., SD) into a set of component traits that were present in HTP data but not previously exploited.

AI/ML↗

Tomographic FLEET with a wedge array for multi-point three-component velocimetry

Femtosecond laser electronic excitation tagging (FLEET) velocimetry is an important diagnostic technique for seedless velocimetry measurements particularly in supersonic and hypersonic flows. Typical FLEET measurements feature a single laser line and camera system to achieve one-component velocimetry along a line, although some multiple-spot and multiple-component configurations have been demonstrated. In this work, tomographic imaging is used to track the three-dimensional location of many FLEET spots. A quadscope is used to combine four unique views onto a single high-speed image intensifier and camera. Tomographic reconstructions of the FLEET emission are analyzed for three-component velocimetry from multiple FLEET spots. Glass wedges are used to create many (nine) closely spaced FLEET spots with less than 10% transmission losses. These developments lead to a significant improvement in the dimensionality and spatial coverage of a FLEET instrument with some increases in experimental complexity and data processing. Multiple-point three-component FLEET velocimetry is demonstrated in an underexpanded jet.

47 OTHER INSTRUMENTATION↗

Statistical mechanics and pressure of composite multimoded weakly nonlinear optical systems

Statistical mechanics can provide a versatile theoretical framework for investigating the collective dynamics of weakly nonlinear-wave settings that can be utterly complex to describe otherwise. In optics, composite systems arise due to interactions between different frequencies and polarizations. The purpose of this work is to develop a thermodynamic theory that takes into account the synergistic action of multiple components. We find that the type of the nonlinearity involved can have important implications in the thermalization process and, hence, can lead to different thermal equilibrium conditions. Importantly, we derive closed-form expressions for the actual optomechanical pressure that is exerted on the system. In particular, the total optomechanical pressure is the sum of the partial pressures due to each component. Our results can be applied to a variety of weakly nonlinear optical settings such as multimode fibers, bulk waveguides, photonic lattices, and coupled microresonators. We present two specific examples, where two colors interact in a one-waveguide array with either a cubic or quadratic nonlinearity.

Efremidis, Nikolaos K. (ORCID:0000000298300268)↗

Multiple emission components in the Cygnus cocoon detected from Fermi -LAT observations

Star-forming regions may play an important role in the life cycle of Galactic cosmic rays (CRs), notably as home to specific acceleration mechanisms and transport conditions. Gamma-ray observations of Cygnus X have revealed the presence of an excess of hard-spectrum gamma-ray emission, possibly related to a cocoon of freshly accelerated particles. We seek an improved description of the gamma-ray emission from the cocoon using ~13 yr of observations with the Fermi-Large Area Telescope (LAT) and use it to further constrain the processes and objects responsible for the young CR population. We developed an emission model for a large region of interest, including a description of interstellar emission from the background population of CRs and recent models for other gamma-ray sources in the field. Thus, we performed an improved spectro-morphological characterisation of the residual emission including the cocoon. The best-fit model for the cocoon includes two main emission components: an extended component FCES G78.74+1.56, described by a 2D Gaussian of extension r 68 = 4.4° ± 0.1° -0.1° +0.1° and a smooth broken power law spectrum with spectral indices 1.67 ± 0.05 -0.01 +0.02 and 2.12 ± 0.02 -0.01 +0.00 below and above 3.0 ± 0.6 -0.2 +0.0 GeV, respectively; and a central component FCES G80.00+0.50, traced by the distribution of ionised gas within the borders of the photo-dissociation regions and with a power law spectrum of index 2.19 ± 0.03 -0.01 +0.00 that is significantly different from the spectrum of FCES G78.74+1.56. An additional extended emission component FCES G78.83+3.57, located on the edge of the central cavities in Cygnus X and with a spectrum compatible with that of FCES G80.00+0.50, is likely related to the cocoon. For the two brightest components FCES G80.00+0.50 and FCES G78.74+1.56, spectra and radial-azimuthal profiles of the emission can be accounted for in a diffusion-loss framework involving one single population of non-thermal particles with a flat injection spectrum. Particles span the full extent of FCES G78.74+1.56 as a result of diffusion from a central source, and give rise to source FCES G80.00+0.50 by interacting with ionised gas in the innermost region. For this simple diffusion-loss model, viable setups can be very different in terms of energetics, transport conditions, and timescales involved, and both hadronic and leptonic scenarios are possible. The solutions range from long-lasting particle acceleration, possibly in prominent star clusters such as Cyg OB2 and NGC 6910, to a more recent and short-lived release of particles within the last 10–100 kyr, likely from a supernova remnant. The observables extracted from our analysis can be used to perform detailed comparisons with advanced models of particle acceleration and transport in star-forming regions.

79 ASTRONOMY AND ASTROPHYSICS↗

HotCell Components

To support operations in the HFEF Hot Cell and the FCF Hot Cell, multiple components were designed to support the Bottle Cutter Project and the Sodium Loop Project. These components were designed and analyzed to ensure they can function in their environment and be operated remotely. FEA analysis were performed, prototypes were made, and parts were manufactured to complete this project. This poster documents these parts and test results.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Cascade of vestigial orders in two-component superconductors: Nematic, ferromagnetic, s -wave charge- 4 e , and d -wave charge- 4 e states

Electronically ordered states that break multiple symmetries can melt in multiple stages, similarly to liquid crystals. In a partially melted phase, known as vestigial phase, a bilinear made out of combinations of the multiple components of the primary order parameter condenses. Multicomponent superconductors are thus natural candidates for vestigial order since they break both the U⁡(1)-gauge and also time-reversal or lattice symmetries. Here, we use group theory to classify all possible real-valued and complex-valued bilinears of a generic two-component superconductor on a tetragonal or hexagonal lattice. While the more widely investigated real-valued bilinears correspond to vestigial nematic or ferromagnetic order, the little explored complex-valued bilinears correspond to a vestigial charge-4e condensate, which itself can have an underlying s-wave, d x 2 –y 2 -wave, or d xy -wave symmetry. To properly describe the fluctuating regime of the superconducting Ginzburg-Landau action and thus access these competing vestigial phases, we employ both a large-N and a variational method. We show that while vestigial order can be understood as a weak-coupling effect in the large-N approach, it is akin to a moderate-coupling effect in the variational method. Despite these distinctions, both methods yield similar results in wide regions of the parameter space spanned by the quartic Landau coefficients. Specifically, we find that the nematic and ferromagnetic phases are the leading vestigial instabilities, whereas the various types of charge-4⁢e order are attractive albeit subleading vestigial channels. The only exception is for the hexagonal case, in which the nematic and s-wave charge-4e vestigial states are degenerate. Finally, we discuss the limitations of our approach, as well as the implications of our results for the realization of exotic charge-4⁢e states in material candidates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Two-component nematic superconductivity in 4Hb-TaS 2

Most superconductors have an isotropic, single component order parameter and are well described by the standard (BCS) theory for superconductivity. Unconventional, multiple-component superconductors are exceptionally rare and are much less understood. Here, we combine scanning tunneling microscopy and angle-resolved macroscopic transport for studying the candidate chiral superconductor, 4Hb-TaS 2 . We reveal quasi-periodic one-dimensional modulations in the tunneling conductance accompanied by two-fold symmetric superconducting critical field. The strong modulation of the in-plane critical field, H c2 , points to a nematic, unconventional order parameter. However, the imaged vortex core is isotropic at low temperatures. We suggest a model that reconciles this apparent discrepancy and takes into account previously observed spontaneous time-reversal symmetry breaking at low temperatures. The model describes a competition between a dominating chiral superconducting order parameter and a nematic one. The latter emerges close to the normal phase. Our results strongly support the existence of two-component superconductivity in 4Hb-TaS 2 and can provide valuable insights into other systems with coexistent charge order and superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Validation and Verification of TEDS Facility HYBRID Modeling

The HYBRID modeling repository is an in-house developed library of models for selected integrated energy systems (IES) modelling. HYBRID models have been developed since 2015 to describe the physical operation of tightly coupled thermal systems including power generators, thermal transport systems, thermal storage, thermal-to-electric conversion systems, and other thermal applications. Here, validation and verification (V&V) capabilities are demonstrated using the Thermal Energy Distribution System (TEDS) at INL. Building upon prior work, the TEDS model has been updated and verified so that it better represents the installed system configuration and the operating control system. The model control system was changed to allow replication of actual experimental procedures. Experimental operations focusing primarily on thermocline tank performance were devised and performed. Several anomalies were found in the operation data of the experiment facility. V&V activities calibrating a selected input parameter are demonstrated on a single component as well as with a single parameter within the thermocline. Calibrating is then demonstrated on multiple components and a multi-parameter metric for the entire system. The validation methodology is successfully applied to validate the model with experimental data. It is also used to confirm a hypothesis behind one of the anomalies in experimental performance.

25 ENERGY STORAGE↗

Development of a Novel Magnesium Alloy for Thixomolding® of Automotive Components (Final Report)

Magnesium (Mg) alloy die-castings are increasingly used in the automobile industry to achieve cost effective mass reduction, especially in systems where multiple components can be integrated into a single thin wall die-casting. However, there are several component quality restrictions in thin-walled Mg die castings, including variability in dimensional accuracy, part-to-part variation in mechanical properties, and porosity in the final part, which has limited the continued growth of die-cast components in the automobile industry. An alternative to die-casting is the process of thixomolding®. While the die-casting process relies on filling a mold at high speeds with the alloy in the completely molten state, the thixomolding® process fills a mold with a thixotropic alloy in a semi-solid slurry state at a temperature between the liquidus and solidus temperatures. Ideally, the material should be ~30–65% solid rather than being completely liquid at the beginning of the injection process. Advantages of the thixomolding® process include a finer grain structure, lower porosity, improved dimensional accuracy, improved part-to part consistency, improved mechanical properties, particularly ductility in the component, the ability to reduce wall thickness for mass savings, and longer tool life due to lower process temperatures. The objective of this collaborative project between Oak Ridge National Laboratory, FCA US LLC, and Leggera Technologies was to develop one or more novel Mg alloys more suitable for thixomolding® automotive structural components than the current die-casting alloys used for this process. The primary interest was to improve ductility while maintaining tensile and fatigue strengths, as these are properties that are critical for use in body and chassis structural applications. Since good corrosion resistance is also desirable for this application, this property was also considered when evaluating promising alloy compositions. An initial evaluation of existing components thixomolded® using AM60 was performed and microstructure, and tensile properties were evaluated for the baseline alloy. Targets were established for ease of processing (characterized by the melting range defined as the difference between the liquidus and the solidus), strength, and ductility. Computational modeling was used to identify promising alloys and selected alloys were cast in laboratory scale heats. Properties measured from laboratory scale heats were used to down-select two alloys for further evaluation and component fabrication. Two alloys were prepared in industrial scale heats, cut into small pieces (chips), and thixomolding® trials were initiated. Trial components were successfully fabricated using one alloy composition, but it was concluded that further refinement of the thixomolding® process parameters are required to successfully fabricate component using second alloy. Microstructure and mechanical properties were evaluated on the material removed from the fabricated component and properties were compared to the baseline alloy. Although mechanical properties of the alloys showed improvement over the baseline alloy, it was determined that modifications to the thixomolding® process would result in better microstructure control with further improvement in properties leading to successful commercialization. A provisional patent application has already been filed on the new alloys developed as part of the project.

36 MATERIALS SCIENCE↗

Effects of forest structural and compositional change on forest microclimates across a gradient of disturbance severity

Forest structural diversity and community composition are key in regulating forest microclimates. When disturbance affects structural diversity or composition, forest microclimates may be altered due to changes in soil temperature, soil water content, and light availability. It is unclear however which structural or compositional components, when changed or to what extent, result in microclimatic change. To address this question, we used data from a large scale, manipulative stem-girdling experiment in northern, lower Michigan—the Forest Resilience and Threshold Experiment (FoRTE). FoRTE follows a factorial design with multiple levels of disturbance severity (0, 45, 65, 85%) based on targeted reductions in gross leaf area index via stem-girdling induced mortality. These disturbance severity treatments are applied in two ways: either as top-down (largest trees are killed) or bottom-up (small to medium trees killed) treatments. We examined how multiple components of structural diversity and community composition changed as a product of disturbance severity and type, and then tested for resulting effects on forest microclimates (light availability, soil temperature, and soil water), using a multivariate, Random Forest framework. We found that measures of community composition (species richness, species evenness, and Shannon-Wiener Diversity Index) and stand structure (basal area, standard deviation of DBH, tree size diversity) declined more following disturbance than did measures of canopy cover, heterogeneity, arrangement, or height. However, when changes in each variable from pre- to post-disturbance, measured as log change, were employed in a multivariate, Random Forest regression framework, structural diversity measures of heterogeneity (rugosity, top rugosity), cover (canopy cover), and arrangement (porosity) were the most influential variables, but with differences among bottom-up and top-down treatments We found that the death of large trees from disturbance impacts soil temperature, water, and light environments more substantially and uniformly across disturbance gradients than does the death of smaller trees. Furthermore, our results have implications for both statistical and process-based modeling of forest disturbance.

54 ENVIRONMENTAL SCIENCES↗

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗

Equilipy: a python package for calculating phase equilibria

The CALPHAD (CALculation of PHAse Diagram) approach (Nigel Saunders & Miodownik, 1998) provides predictions for thermodynamically stable phases in multicomponent-multiphase materials across a wide range of temperatures. Consequently, the CALPHAD calculations became an essential tool in materials and process design (Luo, 2015). Such design tasks frequently require navigating a high-dimensional space due to multiple components involved in the system. This increasing complexity demands high-throughput CALPHAD calculations, especially in the rapidly evolving field of alloy design. In response to the need, we developed Equilipy an open-source Python package designed for calculating phase equilibria of multicomponent-multiphase systems. Equilipy is specifically tailored for high-throughput CALPHAD calculations, offering parallel computations across multiple processors and nodes with the given NPT input conditions namely elemental compositions (N), pressure (P), and temperature (T). Equilipy utilizes the program structure and Gibbs energy functions from the Fortran-based program, Thermochimica (Piro et al., 2013), with incorporating a new Gibbs energy minimization algorithm. This algorithm, originally developed by Capitani and Brown in 1987 (Capitani & Brown, 1987), has been revised and implemented to enhance the stability and performance of calculations. The Fortran codes are precompiled and interfaced with Python via F2PY, ensuring high computation speed. Benchmark tests shown in Figure 1 demonstrate that Equilipy’s computation speed is comparable to those of established commercial software, TC-Python and PanPython. This result highlights its efficiency and potential applications in various scientific and industrial fields.

97 MATHEMATICS AND COMPUTING↗

Synthesis of Correct Digital Controller Models from Specifications by Model Transformation (21-0320)

The design of high consequence controllers (in weapons systems, autonomy, etc.) that do what they are supposed to do is a significant challenge. Testing simply does not come close to meeting the requirements for assurance. Today circuit designers at Sandia (and elsewhere) typically capture the core behavior of their components using state models in tools such as STATEFLOW. They then check that their models meet certain requirements (e.g. “The system bus must not deadlock” or “both traffic lights at an intersection must not be green at the same time”) using tools called model checkers. If the model checker returns “yes” then the property is guaranteed to be satisfied by the model. However, there are several drawbacks to this industry practice: (1) there is a lot of detail to get right, this is particularly challenging when there are multiple components requiring complex coordination (2) any errors returned by the model checker have to be traced back through the design and fixed, necessitating rework, (3) there are severe scalability problems with this approach, particularly when dealing with concurrency. All this places high demands on the designers who now face not only an accelerated schedule but also controllers of increasing complexity. This report describes a new and fundamentally different approach to the construction of safety-critical digital controllers. Instead of directly constructing a complete model and then trying to verify it, the designer can start with an initial abstract (think “sketch”) model plus the requirements, from which a correct concrete model is automatically synthesized. There is no need for post-hoc verification of required functional properties. Having tool to carry this out will significantly impact the nation’s ability to ensure the safety of high-consequence digital systems. The approach has been implemented in a prototype tool, along with a suite of examples, including ones that reflect actual problems faced by designers. Our approach operates on a variant of Statecharts developed at Sandia called Qspecs. Statecharts are a widely used formalism for developing concurrent reactive systems, supporting scalability through allowing state models containing composite states, which are the serial or parallel composition of substates which can themselves contain statecharts. Statecharts enable an incremental style of development, in which states are progressively refined to incorporate greater detail in an incremental model of software development. Our approach formulates a set of constraints from the structure of the models and the requirements and propagates these constraints to a fixpoint. The solution to the constraints is an inductive invariant along with guards on the transitions. We also show how our approach extends to implementation refinement, decomposition, composition, and elaboration. We currently handle safety requirements written in LTL (Linear Temporal Logic)

42 ENGINEERING↗

Chiral Spectroscopy of Nanostructures

Chirality is ubiquitous in the universe and in living creatures over detectable length scales from the subatomic to the galactic, as exemplified in the two extremes by subatomic particles (neutrinos) and spiral galaxies. Between them are living creatures that display multiple levels of chirality emerging from hierarchically assembled asymmetric building blocks. Not too far from the bottom of this pyramid are the foundational building blocks with chiral atomic centers on sp 3 carbon atoms exemplified by L-amino acids and D-sugars that are self-assembled into higher-order structures with increasing dimensions forming highly complex, amazingly functional, and energy-efficient living systems. The organization and materials employed in their construction inspired scientists to replicate complex living systems via the self-assembly of chiral components. Multiple studies pointed to unexpected and unique electromagnetic properties of chiral structures with nanoscale and microscale dimensions, including giant circular dichroism and collective circularly polarized scattering that their constituent units did not possess. To address the wide variety of chiral geometries observed in continuous materials, singular particles, and their complex systems, multiple analytic techniques are needed. Simultaneously, their spectroscopic properties create a pathway to multiple applications. For example, mirror-asymmetric vibrations at chiral centers formed by sp 3 carbon atoms lead to optical activity for the infrared (IR) wavelength regions. At the same time, understanding the optical activity in, for example, the IR region enables biomedical applications because multiple modalities of biomedical imaging and vibrational optical activity (VOA) of biomolecules are known for IR range. In turn, VOA can be realized in both absorption and emission modalities due to large magnetic transition moments, as vibrational circular dichroism (VCD) or Raman optical activity (ROA) spectroscopy. In addition to the VOA, in the range of longer wavelengths, lattice vibrational mode or phononic behavior occurs in chiral crystals and nanoassemblies, which can be readily detected by terahertz circular dichroism (TCD) spectroscopy. Meanwhile, chiral self-assembly can induce circularly polarized light emission (CPLE) regardless of the existence of chirality in coassembled fluorophores. The CPLE from self-assembled chiral materials is particularly interesting because the CPLE can originate from both circularly polarized luminescence and circularly polarized scattering (CPS). Furthermore, because self-assembled nanostructures often exhibit stronger optical activity than their building blocks owing to dimension and resonance effects, the optical activity of single assembled nanostructures can be investigated by using microscopic technology combined with chiral optics. Here, we describe the state of the art for spectroscopic methods for the comprehensive analysis of chiral nanomaterials at various photon wavelengths, addressed with special attention given to new tools emerging both for materials with self-organized hierarchical chirality and single-particle spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Reinforcement Learning Control for Buildings Co-Optimizing Energy, Comfort, and Indoor Air Quality: An Annual Assessment

Efficient control of Heating, Ventilation, and Air Conditioning (HVAC) systems is crucial for optimizing energy use and maintaining indoor comfort in buildings. Traditional control methods, such as PID control, cannot handle energy use trade-offs among multiple components in the building energy system at a supervisory level. Reinforcement learning (RL) presents a promising solution, offering adaptive and data-driven control strategies that optimize performance over time. However, RL also faces several challenges, including the conflicts encountered in co-optimizing energy savings, occupant comfort, and indoor air quality, and the requirement for extensive interactions with the environment in training. We proposed a flexible simulation platform that integrates a hybrid model for RL training and designed an RL agent to control the entire central HVAC system, focusing on co-optimizing energy consumption, thermal comfort, and indoor air quality ($\text{CO}_{2}$ and PM2.5 concentrations). Finally, we evaluated the RL agent's performance over an annual cycle. Our findings indicate that the RL agent can effectively manage the HVAC system with 14.7 % energy savings annually and balance multiple objectives, which demonstrates significant potential for improving HVAC system control and sustainability in buildings.

Guo, Fangzhou↗