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At least 127 records · Page 7

Vapor Phase Infiltration of Titanium Oxide into P3HT to Create Organic–Inorganic Hybrid Photocatalysts

Herein, we report for the first time the use of vapor phase infiltration (VPI) to infuse conducting polymers with inorganic metal oxide clusters that together form a photocatalytic material. While vapor infiltration has previously been used to electrically dope conjugated polymers, this is the first time, to our knowledge, that the resultant hybrid material has been demonstrated to have photocatalytic properties. The system studied is poly(3-hexylthiophene-2,5-diyl) (P3HT) vapor infiltrated with TiCl 4 and H 2 O to create P3HT-TiO x organic–inorganic hybrid photocatalytic materials. X-ray photoelectron spectroscopy analysis shows that P3HT-TiO x VPI films consist of a partially oxidized P3HT matrix, and the infiltrated titanium inorganic is in a 4+ oxidation state with mostly oxide coordination. Upon visible light illumination, these P3HT-TiO x hybrids degrade methylene blue dye molecules. The P3HT-TiO x hybrids are 4.6× more photocatalytically active than either the P3HT or TiO 2 individually or when sequentially deposited (e.g., P3HT on TiO 2 ). On a per surface area basis, these hybrid photocatalysts are comparable or better than other best in class polymer semiconductor photocatalysts. VPI of TiCl 4 + H 2 O into P3HT makes a unique hybrid structure and idealized photocatalyst architecture by creating nanoscale TiO x clusters concentrated toward the surface achieving extremely high catalytic rates. The mechanism for this enhanced photocatalytic rate is understood using photoluminescence spectroscopy, which shows significant quenching of excitons in P3HT-TiO x as compared to neat P3HT, indicating that P3HT acts as a photosensitizer for the TiO x catalyst sites in the hybrid material. This work introduces a new approach to designing and synthesizing organic–inorganic hybrid photocatalytic materials, with expansive opportunities for further exploration and optimization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Active learning path-dependent properties using a cloud-based materials acceleration platform

Solid state materials are central to many modern technologies in which a given material may be exposed to a variety of environments. The material properties often vary with the sequence of environments in an irreversible manner, resulting in a quintessential path-dependency in experimental observables. While sequential learning techniques have been effectively deployed for accelerating learning of state properties of materials, they often use a consistent environment path in all experiments. To elevate such techniques for making optimal decisions in experimental investigations of path-dependent properties, we introduce an iterated expected information gain acquisition function that optimizes over entire experimental trajectories. This approach is implemented within a cloud-based Materials Acceleration Platform architecture utilizing an event-driven stateful broker coupled with remote HELAO (Hierarchical Experimental Laboratory Automation and Orchestration) instances and an AI science manager. The platform's efficacy was demonstrated through a case study optimizing multi-step spectro-electrochemical experiments to identify optically stable potential windows in (Co–Ni–Sb)O z metal oxides. The system successfully integrated AI-driven experiment design, remote laboratory automation, and cloud-based data infrastructure, validating the platform's capability for managing complex, adaptive, path-dependent workflows in materials discovery.

Guevarra, Dan [California Institute of Technology ↗

Fabrication and Testing of DOE Standard Canister Closure Leak Test Assembly

DOE manages over 300 types of spent nuclear fuel (SNF), many of which are located at the Idaho National Laboratory (INL) site. Managing this large variety of SNF for storage, transportation, and disposal poses a challenge to DOE. The Idaho Cleanup Project and INL are collaborating on the DOE SNF Road-Ready Demonstration (“Road-Ready Demonstration”), which will develop and demonstrate the designs, technology, processes, and regulatory framework for packaging DOE-managed SNF for “road-ready dry storage.” Road-ready dry storage is an SNF management concept in which SNF is packaged into dry, sealed canisters that are then placed in onsite storage in anticipation of later transport and disposition. The forward-looking goal of the Road-Ready Demonstration is to establish the foundation for a large-scale road-ready dry storage program at the INL site. In support of establishing a large-scale road-ready dry storage program at the INL site, the Road-Ready Demonstration will first package Fort St. Vrain SNF currently in dry storage at INL into several DOE Standard Canisters (DOESCs). These DOESCs will in turn be loaded into another containment similar to commercial multi-purpose canisters. This multi-purpose canister will then be compatible with a transportation or storage system, such as a storage cask for interim storage or transportation package for offsite transport. These DOESCs will remain sealed over the course of their storage, transportation, and applicable disposal functions. The closure process for the DOESC will include fuel and basket loading, welding, inspection, leak testing, and, if needed, repair. As a follow-up to previous discussions on the design of the DOE Closure Leak Test Assembly (LTA), this report describes recent fabrication and testing efforts performed at INL. DOESCs are sealed by two sequential gas tungsten arc welds, both of which are performed by remotely operated and semiautomatic welding systems. The first weld is a circumferential pipe weld that completes the assembly of the canister body and lid assembly. The second and final closure weld attaches the vent plug to the vent socket via a butt joint. After the second weld is performed, the welds are helium leak tested using an evacuated envelope technique. The LTA was designed for both remote and manual operation. This report describes the fabrication and performance testing associated with the evacuated envelope technique. INL staff designed, fabricated, and tested the LTA at INL facilities. This testing included establishing technique and system sensitivities in accordance with ASME and American National Standards Institute N14.5 requirements. Forthcoming work will cover such areas as design optimization, process and personnel qualification, and implementation in Road-Ready Demonstration operations.

42 ENGINEERING↗

Fabrication and Testing of DOE Standard Canister Closure Leak Test Assembly – 24139

DOE manages over 300 types of spent nuclear fuel (SNF), many of which are located at the Idaho National Laboratory (INL) site. Managing this large variety of SNF for storage, transportation, and disposal poses a challenge to DOE. The Idaho Cleanup Project and INL are collaborating on the DOE SNF Road-Ready Demonstration (“Road-Ready Demonstration”), which will develop and demonstrate the designs, technology, processes, and regulatory framework for packaging DOE-managed SNF for “road-ready dry storage.” Road-ready dry storage is an SNF management concept in which SNF is packaged into dry, sealed canisters that are then placed in onsite storage in anticipation of later transport and disposition. The forward-looking goal of the Road-Ready Demonstration is to establish the foundation for a large-scale road-ready dry storage program at the INL site. In support of establishing a large-scale road-ready dry storage program at the INL site, the Road-Ready Demonstration will first package Fort St. Vrain SNF currently in dry storage at INL into several DOE Standard Canisters (DOESCs). These DOESCs will in turn be loaded into another containment similar to commercial multi-purpose canisters. This multi-purpose canister will then be compatible with a transportation or storage system, such as a storage cask for interim storage or transportation package for offsite transport. These DOESCs will remain sealed over the course of their storage, transportation, and applicable disposal functions. The closure process for the DOESC will include fuel and basket loading, welding, inspection, leak testing, and, if needed, repair. As a follow-up to previous discussions on the design of the DOE Closure Leak Test Assembly (LTA), this report describes recent fabrication and testing efforts performed at INL. DOESCs are sealed by two sequential gas tungsten arc welds, both of which are performed by remotely operated and semiautomatic welding systems. The first weld is a circumferential pipe weld that completes the assembly of the canister body and lid assembly. The second and final closure weld attaches the vent plug to the vent socket via a butt joint. After the second weld is performed, the welds are helium leak tested using an evacuated envelope technique. The LTA was designed for both remote and manual operation. This report describes the fabrication and performance testing associated with the evacuated envelope technique. INL staff designed, fabricated, and tested the LTA at INL facilities. This testing included establishing technique and system sensitivities in accordance with ASME and American National Standards Institute N14.5 requirements. Forthcoming work will cover such areas as design optimization, process and personnel qualification, and implementation in Road-Ready Demonstration operations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Liquid Piston with Spray Cooling Near-Isothermal Compressor

The goal of this project was to prototype and characterize the performance of a liquid-piston spray-cooled gas compressor. The working principle of the compressor enables optimized high-efficiency operation over a very wide range of operating conditions, unlike conventional compressors that are optimized for a narrow range of operating conditions. The compressor technology is suitable for many applications, such as gas pipeline transport, gas storage, and commercial and residential heat pumps. Both physical testing and computational fluid dynamics (CFD) modeling of the processes using the Oak Ridge National Laboratory high-performance computing center were completed. The experimental and CFD studies focused on a near-isothermal liquid-piston compressor (LPC) that uses propylene glycol to compress CO 2 . The first prototype demonstrated isothermal operation during several sequentially executed cycles of CO 2 compression and raised the temperature of the compressed CO 2 by only 2 K, compared with approximately 6 K when the gas was compressed non-isothermally. Isothermal operation was demonstrated at CO 2 flow rates of up to 2 L/min. The second prototype was designed with two compression chambers to allow continuous flow of high-pressure CO 2 . However, the design of the valve train to direct flow between the compression chambers was not sufficient to allow demonstration of CO 2 compression. Numerical simulations of the LPC in which the compression chamber was filled with propylene glycol injected from the bottom inlet were performed using large eddy simulation (LES) with the wall-adapting local eddy-viscosity subgrid-scale model coupled with the multiphase volume of fluid (VOF) model to simulate the transient interface between gas and liquid and to capture the heat and mass transfers within the compression chamber. In this effort, the effects of boundary conditions applied to the LES-VOF calculations (i.e., no wall, an adiabatic wall, and a wall with a heat flux subscribed) on the overall pressure and temperature of the CO 2 gas as well as the transient evolution of flow and heat transfer within the compression chamber were investigated and are discussed in this report. The LES calculation with no wall showed no dynamical flow patterns, and the volume-averaged temperature of CO 2 increased from 305 to 392.7 K, whereas LES calculations with a constant wall temperature or a wall heat flux had similar increases of CO 2 temperatures. The results of the LES simulation using a wall heat flux showed different stages in the compression process and revealed dynamical formation and interaction of CO 2 gas layers and circulation flow patterns within the chamber that contributed to the overall heat transfer between the solid wall, gas, and liquid surface in the compressor. Though an industrial partnership for commercializing the compressor was not secured, the technology was attractive for an industrial partner to use in two research proposals in response to US Department of Energy funding opportunity announcements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Fabrication and Testing of DOE Standard Canister Closure Leak Test Assembly

DOE manages over 300 types of SNF, most of which are located at the INL site. “Road-ready dry storage” is a management concept where SNF is packaged into dry, sealed canisters, which are then placed in on-site storage in anticipation of later removal. The Idaho Cleanup Project and INL are collaborating on the Road-Ready Capability Demonstration Project, which will develop and demonstrate the designs, technology, processes, and regulatory framework for packaging DOE SNF for road-ready dry storage. In support of establishing a large-scale road-ready dry storage program at the INL site, the demonstration will package a select amount of DOE-managed SNF into DOE Standard Canisters. The closure process for the DOE Standard Canisters will include fuel and basket loading, welding, inspection, leak testing, and if needed, repair. As a follow-up to previous discussion on the design of the DOE Closure Leak Test Assembly, this report describes recent fabrication and testing efforts performed at INL. DOE Standard Canisters are sealed by two sequential gas tungsten arc welds. Both are performed by remotely operated and semi-autonomous welding systems. The first weld is a circumferential pipe weld that completes assembly of the canister body and lid assembly. The second and final closure weld connects the vent plug to the vent port with an identical butt joint to the circumferential pipe weld. After the second weld is performed on the vent port, these welds are helium leak tested using an inside-out technique. In addition to the commercially available vacuum and leak detector systems, the DOE Standard Canister Closure Leak Test Assembly was designed for both remote and manual operation. This report describes fabrication and performance testing associated with the inside-out technique. INL staff designed and tested systems to accomplish these tasks. Hardware fabrication occurred at INL facilities. Forthcoming work includes design optimization, integration to existing systems, and implementation to packaging demonstration operations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Phenomena-based graph representations and applications to chemical process simulation

Rapid and robust simulation of chemical processes is critical to conduct process design, optimization, techno-economic analysis, and sustainability analysis. Yet, efficiently solving simulation models remains a challenge due to the highly coupled and nonlinear nature of the underlying algebraic equations that capture the physical phenomena taking place in the process (e.g., material and energy conservation, phase equilibrium, reactions). In this work, we show that graph-theoretic representations of the physical phenomena within unit operations can help navigate and decompose equations to systematically identify alternative approaches for fast and robust numerical solutions. Specifically, we present a graph-theoretic abstraction that captures the connectivity between the model variables/equations and use this abstraction to group variables/equations into fundamental phenomena. We show that phenomena-based decomposition of the underlying equations can help decouple nonlinearities and enforce material/energy conservation at the process level to accelerate convergence. The proposed decomposition approach differs from the more traditional sequential modular simulation approach, in which equations are grouped and decomposed by unit operations. We implemented the phenomena-based decomposition in BioSTEAM—an open-source process simulation platform in Python—and demonstrated that this approach can converge a variety of separation process models. Compared to sequential modular simulation, the phenomena-based approach can converge idealized systems faster, but it can be slower for (or even fail to converge) highly coupled and nonideal process systems.

Convergence↗

Shoulder fillet effects in strength distributions of microelectromechanical system components

The failure forces and fracture strengths of polysilicon microelectromechanical system (MEMS) components in the form of stepped tensile bars with shoulder fillets were measured using a sequential failure chain methodology. Approximately 150 specimens for each of four fillet geometries with different stress concentration factors were tested. The resulting failure force and strength distributions of the four geometries were related by a common sidewall flaw population existing within different effective stressed lengths. The failure forces, strengths, and flaw population were well described by a weakest-link based analytical framework. Finite element analysis was used to verify body-force based expressions for the stress concentration factors and to provide insight into the variation of specimen effective length with fillet geometry. Monte Carlo simulations of flaw size and location, based on the strength measurements, were also used to provide insight into fillet shape and size effects. The successful description of the shoulder fillet specimen strengths provides further empirical support for application of the strength and flaw framework in MEMS fabrication and design optimization.

36 MATERIALS SCIENCE↗

A Difluoro‐Methoxylated Ending‐Group Asymmetric Small Molecule Acceptor Lead Efficient Binary Organic Photovoltaic Blend

Abstract Developing a new end group for synthesizing asymmetric small molecule acceptors (SMAs) is crucial for achieving high‐performance organic photovoltaics (OPVs). Herein, an asymmetric small molecule acceptor, BTP‐BO‐4FO, featuring a new difluoro‐methoxylated end‐group is reported. Compared to its symmetric counterpart L8‐BO, BTP‐BO‐4FO exhibits an upshifted energy level, larger dipole moment, and more sequential crystallinity. By adopting two representative and widely available solvent additives (1‐chloronaphthalene (CN) and 1,8‐diiodooctane (DIO)), the device based on PM6:BTP‐BO‐4FO (CN) photovoltaic blend demonstrates a power conversion efficiency (PCE) of 18.62% with an excellent open‐circuit voltage (V OC ) of 0.933 V, which surpasses the optimal result of L8‐BO. The PCE of 18.62% realizes the best efficiencies for binary OPVs based on SMAs with asymmetric end groups. A series of investigations reveal that optimized PM6:BTP‐BO‐4FO film demonstrates similar molecular packing motif and fibrillar phase distribution as PM6:L8‐BO (DIO) does, resulting in comparable recombination dynamics, thus, similar fill factor. Besides, it is found PM6:BTP‐BO‐4FO possesses more efficient charge generation, which yields betterV OC –J SC balance. This study provides a new ending group that enables a cutting‐edge efficiency in asymmetric SMA‐based OPVs, enriching the material library and shed light on further design ideas.

Chemistry↗

OPTIMIZATION OF A NUCLEAR VESSEL OUTLET FOR INCIDENT MONITORING

Classical nuclear core fluidic design techniques require improvement to better align with modern technological innovations. The US Department of Energy’s Office of Nuclear Energy (DOE-NE) Transformational Challenge Reactor (TCR) program is deploying additive manufacturing and advanced modeling and simulation to reimagine these designs. With the aid of modern computing power, computerized design optimization can be implemented to remove unwanted pressure drop while simultaneously optimizing flow structures, resulting in new opportunities to enable advanced instrumentation and monitoring capabilities.Previous development of geometric specifications for the TCR pressure vessel’s outlet plenum used design optimization to (1) limit pressure losses below 3.5 kPa (~0.5 psi) and (2) create a fluidic plane in which the temperature variation would not exceed ±5°C. This significant limit of the allowable pressure drop stems from the overarching goal of the TCR program to apply cutting edge techniques and unconventional thinking to demonstrate potential opportunities in additive manufacturing (AM).This paper expands the previous work by optimizing thermowell locations for robust measurements by explicitly modeling them and the resulting flow impacts. Additionally, a single core coolant channel was chosen to represent an event that causes an increased bulk flow temperature increase of 100°C.High fidelity unsteady Reynolds-averaged Navier-Stokes (URANS) simulations of the conjugate heat transfer problem were run in Siemen’s Star-CCM+ for this study. Next, the bulk flow temperature of a single coolant channel was increased by 100°C and was allowed to converge again. Finally, statistical analysis using a sequential probability ratio test (SPRT) was used to determine the elapsed time the thermocouples took to discover the increased bulk flow temperature.

See, Nate↗

Implementing a unified solver for nonlinearly constrained optimization

SQP and interior-point methods (also referred to as Lagrange-Newton methods) typically share key algorithmic components, such as strategies for computing descent directions and mechanisms that promote global convergence. Building on this insight, we introduce a unifying framework with eight building blocks that abstracts the workflows of Lagrange-Newton methods. We then present Uno, a modular C++ solver that implements our unifying framework and allows the automatic combination of a wide range of strategies with no programming effort from the user. Uno is meant to (1) organize mathematical optimization strategies into a coherent hierarchy; (2) offer a wide range of efficient and robust methods that can be compared for a given instance; (3) enable researchers to experiment with novel optimization strategies; and (4) reduce the cost of development and maintenance of multiple optimization solvers. Uno’s software design allows user to compose new customized solvers for emerging optimization areas such as robust optimization or optimization problems with complementarity constraints, while building on reliable nonlinear optimization techniques. We demonstrate that Uno is highly competitive against state-of-the-art solvers filterSQP, IPOPT, SNOPT, MINOS, LANCELOT, LOQO, and CONOPT on a subset of 429 small problems from the CUTE collection. Uno is available as open-source software under the MIT license at https://github.com/cvanaret/Uno and via its C, Julia, Python, Fortran, and AMPL interfaces.

97 MATHEMATICS AND COMPUTING↗

Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn 2 As 2 , a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Band gap predictions of double perovskite oxides using machine learning

Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E g < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.

36 MATERIALS SCIENCE↗

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗

Graph-Based Representations and Applications to Process Simulation

Rapid and robust convergence of a process flowsheet is critical to enable large-scale simulations that address core scientific questions related to process design, optimization, and sustainability. However, due to the highly coupled and nonlinear nature of chemical processes, efficiently solving a flowsheet remains a challenge. In this work, we show that graph representations of the underlying physical phenomena in unit operations may help identify potential avenues to systematically reformulate the network of equations and enable more robust topology-based convergence of flowsheets. To this end, we developed graph abstractions of the governing equations of vapor-liquid and liquid-liquid equilibrium separation equipment. These graph abstractions consist of a mesh of interconnected variable nodes and equation nodes that are systematically generated through PhenomeNode, a new open-source library in Python developed in this study. We show that partitioning the graph into separate mass, energy, and equilibrium subgraphs can help decouple nonlinearities and guide decomposition algorithms. By employing the graph abstraction on an industrial separation process for separating glacial acetic acid from water, we implemented a new block decomposition scheme in BioSTEAM and demonstrated that this can accelerate convergence over a traditional sequential modular approach.

Distillation↗

Development of NDE/NDT Tools for High-Volume & High-Speed Inspection of CFRP Structures in Automotive Manufacturing

Main advantages of the air-coupled ultrasound testing (ACUT) and electromagnetic testing (EMT) techniques for NDE of CFRP composites were non-contact sensing, scalability for high-speed inspection, cost-effectiveness, and non-hazardous operation. Despite these advantages, no systems that would satisfy the project requirements were commercially available. Hence, one of the major efforts of the Michigan State University (MSU) team at the initial stage of the project was to close this technological gap by developing, optimizing, and validating array sensors that would provide sufficient sensitivity, spatial coverage, and resolution for robust defect detection. Optimization of the ACUT and EMT sensor designs was performed using experimentally validated finite element models. Initial experiments using array probes were conducted on relatively flat CFRP samples. In parallel, the MSU team designed and assembled a portable platform with two robotic arms. The robots were equipped with newly designed sensors that enabled high-speed NDE of curved CFRP parts. Presently, the developed robotic platform can be used as a demo/template NDE system, which is easily adaptable to manufacturing environments and in-line NDE. The ACUT NDE system developed by the MSU team used a high-power 4-channel pulser receiver for parallel data acquisition. The array probes were designed by stacking commercially available ACUT transducers, which operated in the frequency range between 100 kHz and 500 kHz. MSU optimized the excitation procedure and developed wave focusing cones so as to reduce the crosstalk between the transducers and to provide higher pulse repletion frequency (PRF). The through-transmission (TT) and single-side access (SSA) inspection modes were successfully implemented. In the TT-ACUT, structural defects in CFRP were detected by passing ultrasonic waves through the test part. Hence, the ACUT transmitters and receivers needed to be placed on the opposite sides of the test part. In the SSA-ACUT, guided waves (GW) were excited in the test part using the transmitters and were sensed by the receivers from the same side. Multi-channel TT-ACUT and SSA-ACUT provided high-speed NDE, and were successfully validated on CFRP test samples with interlaminar delaminations and other embedded defects The EM techniques developed by the MSU team included: 1) eddy current testing (ECT), 2) capacitive imaging (CI) and hybrid dual-mode imaging. In ECT, structural damage was detected in CFRP using coils sensor arrays. In ECT, the excitation magnetic field is generated by passing an alternating current through a coil, which is placed above the test sample. The excitation field penetrates the conductive sample and induces the eddy currents in its transect. In turn, the eddy currents generate the reaction field, which affects the total field sensed by a coil. Hence, the presence of structural flaws will alter the eddy current flow and the picked-up signal. ECT is mostly sensitive to local changes of the electric conductivity of the test sample, and CFRPs are mostly conductive in the direction of carbon fibers. Hence, ECT was well suited for the detection of fiber damage/fiber irregularities. The MSU team developed printed circuit boards (PCB) with coil sensor arrays optimized for NDE of CFRP. Unlike most commercial probes designed for ECT of metallic structures, the MSU array probes were designed for operation in [1-10] MHz frequency range, which was optimal for low-conductive CFRP. Multiple sensing topologies (coil groups excitation/sensing arrangements) were implemented and successfully validated. Capacitive Imaging (CI) technique developed by MSU was complementary to ECT. In contrast to ECT, which was sensitive to local changes of the electrical conductivity, the CI was sensitive to local changes of the dielectric constant. Therefore, CI could provide information about matrix damage/matrix irregularities in CFRP. The MSU CI sensor arrays were made of multiple circular or rectangular open-plate capacitors printed on PCB. Sensors of this type are not commercially available. In addition to ECT and CI, the MSU team developed a hybrid (dual-mode) inductive/capacitive measurement technique that synergistically combined the benefits of inductive and capacitive sensing for rapid NDE of fiber reinforced polymer (FRP) composite structures. Fiber damage and fiber irregularities in FRPs were detected by configuring hybrid sensors as coil sensors. Similarly, matrix damage, matrix irregularities and interlaminar delaminations were detected by configuring hybrid sensors as capacitive sensors. ECT and CI were performed sequentially by means of electronic switching. Hence, eliminating the need for mounting two separate sensor arrays on the probe. Portable robotic platform was developed by MSU for multi-technique high-speed NDE of CFRP test parts. The platform had two 6-axis robots, which enabled inspection of curved parts in approximately a 6×6×6 ft 3 active scan area. On the software side, the MSU team integrated scripts for NDE hardware control with scripts for robot motion control. MSU also implemented automated path planning for the robots, reconstruction of part’s surfaces via stereovision, 3D rendering of inspection data, and image processing algorithms for enhanced defect detection. Automotive composite parts manufactured by Plasan Composites from Phase I were used to validate the ACUT and EMT techniques on representative testbeds. Among those parts were three X-braces for a Dodge Viper, one composite calibration plaque with known defects at known locations, and four other test sections, including sections from a front splitter, a corner section from a composite hood, and a high-pressure RTM panel made using non crimp fabric. Other test samples included CFRP and GFRP calibration plates with fiber/matrix defects fabricated at MSU/CVRC.

36 MATERIALS SCIENCE↗

Energy efficiency in industrial drying: A hybrid ultrasonic system with a novel dynamic optimization framework

Drying processes are among the most energy-consuming operations in industrial and manufacturing settings, demanding strategic selection, design, and control for enhanced efficiency. Advancing drying technologies is critical for improving sustainability, lowering energy use, reducing carbon emissions, and minimizing waste. This study explores two innovative strategies aimed at transforming drying processes into sustainable, low-carbon systems by reducing energy consumption, minimizing waste, and maintaining a strong emphasis on preserving product quality. The first strategy showcases a sub-pilot scale hybrid ultrasonic-convective dryer for agrifood products. This technology, powered by electricity (process electrification), integrates non-thermal ultrasonic dehydration with convective heating and is presented as a sustainable and energy-efficient solution that enhances eco-friendly practices. The second strategy involves introducing and implementing a novel, multiobjective, mixed integer dynamic optimization technique to determine the optimal time-dependent process parameter values for the drying operation. This optimization technique yields operating conditions that are piecewise constant in time aiming to maximize the energy efficiency of the hybrid ultrasonic-convective dryer while ensuring strict adherence to product quality constraints. By adopting the hybrid ultrasonic-convective dryer, a notable 35% improvement in energy efficiency was achieved compared to conventional hot-air drying systems for drying apple slices. The proposed optimization framework further enhanced energy efficiency by nearly 14% over the most efficient process on the identical testbed, under static operating conditions. The reported enhancements have been experimentally validated. Regarding drying time (thereby improving production yield), the developed hybrid ultrasonic-convective dryer demonstrates as much as a 41% reduction in total processing time, which is further optimized by an additional 10% using our proposed optimization framework. The research outcomes have profound implications for the design and operation of drying systems, encompassing crucial aspects such as process electrification, cost-effectiveness, energy savings, time efficiency, product yield, product quality, and process automation.

Dynamic optimization↗

Mechanistic Understanding and Rational Design of Quantum Dot/Mediator Interfaces for Efficient Photon Upconversion

The semiconductor-nanocrystal-sensitized, three-component upconversion system has made great strides over the past 5 years. The three components (i.e., triplet photosensitizer, mediator, and emitter) each play critical roles in determining the input and output photon energy and overall quantum efficiency (QE). The nanocrystal photosensitizer converts the absorbed photon into singlet excitons and then triplet excitons via intersystem crossing. The mediator accepts the triplet exciton via either direct Dexter-type triplet energy transfer (TET) or sequential charge transfer (CT) while extending the exciton lifetime. Through a second triplet energy-transfer step from the mediator to the emitter, the latter is populated in its lowest excited triplet state. Triplet–triplet annihilation (TTA) between two triplet emitters generates the emitter in its bright singlet state, which then emits the upconverted photon. Quantum dots (QD) have a tunable band gap, large extinction coefficient, and small singlet–triplet energy losses compared to metal–ligand charge-transfer complexes. This high triplet exciton yield makes QDs good candidates for photosensitizers. In terms of driving triplet energy transfer, the triplet energy of the mediator should be slightly lower than the triplet exciton energy of the QD sensitizer for a downhill energy landscape with minimal energy loss. The same energy cascade is also required for the transfer from the mediator to the emitter. Lastly, the triplet energy of the emitter must be slightly larger than one-half of its singlet energy to ensure that TTA is exothermic. Optimization of the sensitizer, mediator, and emitter will lead to an increase in the anti-Stokes shift and the total quantum efficiency. Evaluating each individual step’s efficiency and kinetics is necessary for the understanding of the limiting factors in existing systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗