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At least 667 records · Page 37

Building a Trusted Roaming Hub [Slides]

The Trusted Roaming Hub is a U.S. Department of Energy-backed initiative led by the National Laboratory of the Rockies (NLR) to address one of the most persistent challenges in electric vehicle (EV) charging: fragmented roaming, inconsistent interoperability, and insufficient digital trust across charging networks. As EV adoption accelerates and charging infrastructure scales nationwide, today's many-to-many integration model between eMobility Service Providers (eMSPs) and Charge Point Operators (CPOs) has become increasingly brittle, costly, and difficult to secure. The Trusted Roaming Hub introduces a neutral, cybersecurity-forward "switchboard" architecture that enables standardized, secure, and scalable roaming interactions across the EV charging ecosystem. Rather than replacing existing networks or commercial relationships, the hub acts as a trusted intermediary that enforces consistent identity, authentication, authorization, and routing across participants improving reliability for drivers, lowering integration burden for industry, and creating a foundation for future grid-interactive charging services. This read-ahead provides an overview of the problem the hub is designed to solve, the core functional and security concepts behind the architecture, the value proposition to key stakeholders, and the near-term trajectory of the work.

33 ADVANCED PROPULSION SYSTEMS↗

Scalability of high-performance PDE solvers

Performance tests and analyses are critical to effective high-performance computing software development and are central components in the design and implementation of computational algorithms for achieving faster simulations on existing and future computing architectures for large-scale application problems. In this article, we explore performance and space-time trade-offs for important compute-intensive kernels of large-scale numerical solvers for partial differential equations (PDEs) that govern a wide range of physical applications. We consider a sequence of PDE-motivated bake-off problems designed to establish best practices for efficient high-order simulations across a variety of codes and platforms. We measure peak performance (degrees of freedom per second) on a fixed number of nodes and identify effective code optimization strategies for each architecture. In addition to peak performance, we identify the minimum time to solution at 80% parallel efficiency. The performance analysis is based on spectral and p-type finite elements but is equally applicable to a broad spectrum of numerical PDE discretizations, including finite difference, finite volume, and h-type finite elements.

97 MATHEMATICS AND COMPUTING↗

Basic Design Layout of a Modular Pumped Hydro Teaching Unit

This work was originally completed in July 2015 as a proposal to Santa Fe Community College (SFCC) for their engineering program and a follow up to a feasibility study completed for the Community College the year earlier as indicated below. The work was ahead of its time and funding was hard to obtain to implement a project. This work is now being formally released for distribution to a wider audience based on the evolving circumstances in greater society that now favor this approach with funding more readily available. Due to the distribution outside of SFCC, specific architectural drawing sets of the pertinent buildings called out in the design have been removed. No changes to the design have been made. The design layout utilizing a building is not meant to limit the implementation of the design. The elevation difference required may be obtained with a building, a small hill, jacking up the upper tank, or similar means. The design presented is a basic design, identifying major components and how they are connected together. The user may change components under their own liability. All local applicable codes and standards must be followed when building an actual unit, under the authority of a responsible engineer or owner. Appropriate measures must be followed to protect people from hazards such as electricity, water pressure, and general construction. A leak test to verify water tightness of the system should be conducted prior to any operations. The vision of this design is to allow a class of 10 to 15 students to construct the assembly within a month or so time period, and then learn to operate the assembly at their school location under the guidance of their instructor. This will allow the students to learn about the practical concept of storing electricity with water, and gain a hands on experience of the real world challenges faced with full Modular Pumped Hydro Projects.

13 HYDRO ENERGY↗

Random 3D Interpenetrating Electrode Design for Energy Storage Applications

We introduce the concept of random interpenetrating electrode structures, inspired by spinodal decomposition, as a transformative approach for energy storage applications. While ordered architectures, such as double gyroids and Kelvin cell structures, have long been favored for their structural uniformity and surface area utilization, we demonstrate that random structures offer distinct advantages including shorter diffusion pathways, higher packing density, and enhanced reaction kinetics, particularly at low temperatures. Using a combination of computational and electrochemical analysis, we show that random architecture outperforms their ordered counterparts in scenarios where diffusion dominates. This work represents the first systematic exploration of random structures in energy storage, highlighting their potential to overcome limitations of traditional designs. However, challenges such as structural reproducibility and scalability remain, necessitating further investigation into fabrication techniques and material selection. By establishing a framework to optimize random electrode architectures, we provide critical insights into the interplay between geometry, diffusion, and reaction kinetics. This study not only introduces a new design principle for 3D electrodes but also opens pathways for next-generation energy storage systems that demand both high performance and adaptability to a range of operating conditions.

Materials science↗

MFIX-Exa: A path toward exascale CFD-DEM simulations

MFIX-Exa is a computational fluid dynamics–discrete element model (CFD-DEM) code designed to run efficiently on current and next-generation supercomputing architectures. MFIX-Exa combines the CFD-DEM expertise embodied in the MFIX code—which was developed at NETL and is used widely in academia and industry—with the modern software framework, AMReX, developed at LBNL. The fundamental physics models follow those of the original MFIX, but the combination of new algorithmic approaches and a new software infrastructure will enable MFIX-Exa to leverage future exascale machines to optimize the modeling and design of multiphase chemical reactors.

97 MATHEMATICS AND COMPUTING↗

A High Power Density and High Efficiency Traction Drive Based on a Segmented Inverter and an Axial and Radial Flux Hybrid Machine

Eaton Corporation and Oak Ridge National Laboratory (ORNL) collaborated to develop a high performance traction drive system aimed at increasing power density and overall efficiency. The system integrates Eaton’s hybrid flux motor—combining radial and axial flux paths to convert end-winding and structural elements into torque-producing components—with ORNL’s high-power-density segmented inverter. This motor design achieves higher torque density within the same footprint as traditional radial flux machines by effectively utilizing end turns. ORNL’s segmented inverter reduces the DC bus capacitor requirement by over 50% compared to standard voltage source inverters (VSIs), achieving power densities exceeding 100 kVA/L. The dual-module architecture also enables six-phase operation, aligning with Eaton’s motor design. Together, the motor and inverter offer a compact, efficient traction drive system with the potential for significant reductions in system weight and improvements in powertrain efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A functional modeling approach for quality assurance in metal additive manufacturing

Purpose: Due to the complexity of and variations in additive manufacturing (AM) processes, there is a level of uncertainty that creates critical issues in quality assurance (QA), which must be addressed by time-consuming and cost-intensive tasks. This deteriorates the process repeatability, reliability and part reproducibility. So far, many AM efforts have been performed in an isolated and scattered way over several decades. Here, a systematically integrated holistic view is proposed to achieve QA for AM. Design/methodology/approach: A systematically integrated view is presented to ensure the predefined part properties before/during/after the AM process. It consists of four stages, namely, QA plan, prospective validation, concurrent validation and retrospective validation. As a foundation for QA planning, a functional workflow and the required information flows are proposed by using functional design models: Icam DEFinition for Function Modeling. Findings: The functional design model of the QA plan provides the systematically integrated view that can be the basis for inspection of AM processes for the repeatability and qualification of AM parts for reproducibility. Research limitations/implications: A powder bed fusion process was used to validate the feasibility of this QA plan. Feasibility was demonstrated under many assumptions; real validation is not included in this study. Social implications: This study provides an innovative and transformative methodology that can lead to greater productivity and improved quality of AM parts across industries. Furthermore, the QA guidelines and functional design models provide the foundation for the development of a QA architecture and management system. Originality/value: This systematically integrated view and the corresponding QA plan can pose fundamental questions to the AM community and initiate new research efforts in the in-situ digital inspection of AM processes and parts.

36 MATERIALS SCIENCE↗

Understanding Reliability Trade-Offs in 1T-nC and 2T-nC FeRAM Designs

Ferroelectric random access memory (FeRAM) is a promising candidate for energy-efficient nonvolatile memory, particularly for logic-in-memory and compute-in-memory (CIM) applications. Among the available cell architectures, One-Transistor–n-Capacitor (1T-nC) and two-transistor–n-capacitor (2T-nC) FeRAMs each offer distinct trade-offs in density, scalability, and reliability. In this work, we present a comparative study of these two architectures under both dimensional scaling ( XY/Z shrinkage) and vertical integration (increasing stacked capacitors per cell). Using technology computer-aided design (TCAD) and circuit-level simulations, we analyze how scaling impacts ferroelectric capacitance, parasitic coupling, and floating-node (FN) dynamics, which together dictate sense margin (SM) and read stability. A key mitigation strategy—floating unselected capacitors—is applied to both architectures, effectively decoupling the SM from the number of stacked capacitors and enabling tractable analysis across scaling regimes. Results show that 1T-nC suffers more from charge sharing with the bitline (BL), while 2T-nC benefits from transistor isolation and stronger low-voltage sensing at the cost of increased area. By systematically evaluating these behaviors across scaling directions, this work establishes the reliability trade-offs of 1T-nC and 2T-nC cells and provides design guidelines for high-density, vertically integrated FeRAM systems.

1T-nC↗

Technoeconomic Analysis of Novel PV Plant Designs for Extreme Cost Reductions

This project sought to gain a deeper understanding of the cost and performance of future solar photovoltaic (PV) plant components, including bifacial PV modules, tandem PV modules, increased plant voltage architectures, and module-level power electronics, and how they may be integrated into new PV plant designs to significantly reduce the levelized cost of electricity (LCOE) of PV. This was done through extensive modeling of current PV plants and future technologies in three different locations, informed by a comprehensive literature review and informational interviews to develop performance and cost assumptions for these technologies. Sensitivities were conducted to understand tradeoffs between different design options, such as the added energy from increasing row spacing versus additional land costs. An optimization tool was then created utilizing an evolutionary algorithm to determine an optimal PV plant configuration for a given set of technologies that resulted in minimized plant LCOE based upon typical performance and cost inputs.

14 SOLAR ENERGY↗

Bridging paradigms: Designing for HPC-Quantum convergence

Here, this paper presents a comprehensive software stack architecture for integrating quantum computing (QC) capabilities with High-Performance Computing (HPC) environments. While quantum computers show promise as specialized accelerators for scientific computing, their effective integration with classical HPC systems presents significant technical challenges. We propose a hardware-agnostic software framework that supports both current noisy intermediate-scale quantum devices and future fault-tolerant quantum computers, while maintaining compatibility with existing HPC workflows. The architecture includes a quantum gateway interface, standardized APIs for resource management, and robust scheduling mechanisms to handle both simultaneous and interleaved quantum–classical workloads. Key innovations include: (1) a unified resource management system that efficiently coordinates quantum and classical resources, (2) a flexible quantum programming interface that abstracts hardware-specific details, (3) A Quantum Platform Manager API that simplifies the integration of various quantum hardware systems, and (4) a comprehensive tool chain for quantum circuit optimization and execution. We demonstrate our architecture through implementation of quantum–classical algorithms, including the variational quantum linear solver, showcasing the framework’s ability to handle complex hybrid workflows while maximizing resource utilization. This work provides a foundational blueprint for integrating QC capabilities into existing HPC infrastructures, addressing critical challenges in resource management, job scheduling, and efficient data movement between classical and quantum resources.

97 MATHEMATICS AND COMPUTING↗

Robust Copper-Based Nanosponge Architecture Decorated by Ruthenium with Enhanced Electrocatalytic Performance for Ambient Nitrogen Reduction to Ammonia

Electrochemical conversion of nitrogen to green ammonia is an attractive alternative to the Haber–Bosch process. However, it is currently bottlenecked by the lack of highly efficient electrocatalysts to drive the sluggish nitrogen reduction reaction (N2RR). In this work, we strategically design a cost-effective bimetallic Ru–Cu mixture catalyst in a nanosponge (NS) architecture via a rapid and facile method. The porous NS mixture catalysts exhibit a large electrochemical active surface area and enhanced specific activity arising from the charge redistribution for improved activation and adsorption of the activated nitrogen species. Benefiting from the synergistic effect of the Cu constituent on morphology decoration and thermodynamic suppression of the competing hydrogen evolution reaction, the optimized Ru 0.15 Cu 0.85 NS catalyst presents an impressive N 2 RR performance with an ammonia yield rate of 26.25 μg h –1 mg cat. –1 (corresponding to 10.5 μg h –1 cm –2 ) and Faradic efficiency of 4.39% as well as superior stability in alkaline medium, which was superior to that of monometallic Ru and Cu nanostructures. Additionally, this work develops a new bimetallic combination of Ru and Cu, which promotes the strategy to design efficient electrocatalysts for electrochemical ammonia production under ambient conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Geometric Optimization of an Electrochemical Purification Cell to Prevent Corrosion in CSP Plants During Operation

When exposed to moisture or oxygen, molten chloride salts produce corrosive impurities which degrade containment alloys. As a result, this can significantly decrease the lifetime and increase costs of molten-salt-based systems. To overcome this barrier, we designed and modeled an electrochemical purification cell to remove the corrosive impurity MgOH+. Various reactor architectures, including constant stirred tank reactors (CSTRs) and plug flow reactors (PFRs) were investigated. Steady-state thermoelectric properties were evaluated, allowing assessment of the effects of structure and design parameters such as flow rate, cell length, and cross-sectional area of molten salt. The results suggest that our design could most effectively increase reliability and decrease costs of molten-chloride-salt-based systems by protecting them during continuous operation using an annular plug flow reactor.

chloride salts↗

Flexible silicon photonic architecture for accelerating distributed deep learning

The increasing size and complexity of deep learning (DL) models have led to the wide adoption of distributed training methods in datacenters (DCs) and high-performance computing (HPC) systems. However, communication among distributed computing units (CUs) has emerged as a major bottleneck in the training process. In this study, we propose Flex-SiPAC, a flexible silicon photonic accelerated compute cluster designed to accelerate multi-tenant distributed DL training workloads. Flex-SiPAC takes a co-design approach that combines a silicon photonic hardware platform with a tailored collective algorithm, optimized to leverage the unique physical properties of the architecture. The hardware platform integrates a novel wavelength-reconfigurable transceiver design and a micro-resonator-based wavelength-reconfigurable switch, enabling the system to achieve flexible bandwidth steering in the wavelength domain. The collective algorithm is designed to support reconfigurable topologies, enabling efficient all-reduce communications that are commonly used in DL training. The feasibility of the Flex-SiPAC architecture is demonstrated through two testbed experiments. First, an optical testbed experiment demonstrates the flexible routing of wavelengths by shuffling an array of input wavelengths using a custom-designed spatial-wavelength selective switch. Second, a four-GPU testbed running two DL workloads shows a 23% improvement in job completion time compared to a similarly sized leaf-spine topology. We further evaluate Flex-SiPAC using large-scale simulations, which show that Flex-SiPAC is able to reduce the communication time by 26% to 29% compared to state-of-the-art compute clusters under representative collective operations.

Wu, Zhenguo (ORCID:0000000322847985)↗

Optimized Carbon Fiber Intermediate Development to Enable High-Volume Manufacturing of Lightweight Automotive Composites

The ongoing pursuit of improved fuel economy and reduced greenhouse gas emissions has resulted in sustained interest for lightweight materials technologies. In this context, carbon fiber composites have captured the imagination of automotive engineers due to the potential to achieve substantial mass reduction when compared to traditional steel construction. That stated, the use of carbon fiber composites in automotive has been limited for the most part to premium supercars and other derivative platforms. In these cases, manufacturing costs are less of an obstacle to implementation, and the performance benefits of carbon fiber have enabled production of structures offering more than 50% weight savings. In practice, translating these low-volume demonstrations onto high-volume vehicle platforms has remained challenging. This can be attributed to several factors, with the absence of suitable high throughput production methodologies being a key impediment. To date, structural, crash critical components have relied upon manufacturing techniques born out of the aerospace industry. This has created a disconnect between automotive production systems that are accustomed to manufacturing multiple parts per minute and the aerospace technologies that have cycle times in the order of hours. Consequently, the focus of this project is the development of manufacturing process technology for carbon fiber composites that can support a mainstream vehicle program at an assumed throughput of 100,000 vehicles per year. In practice, this translates to a part-to-part cycle time of less than 3 minutes. Project participation included contributions from a broad range of academic, industrial and national lab partners. The primary scope of work, being the development of new carbon fiber epoxy compounds that are stable at room temperature and suited to high throughput automated processing. For project management, the work streams were divided into six key areas, with the lead organization in parentheses. • Carbon fiber/epoxy materials formulation development and scale up (Dow) • Simulation of discontinuous near isotropic meso-structure intermediates (Purdue) • Simulation of mechanical performance of compression molded components (Purdue) • Meso-Scale morphological analysis and correlation with structural performance (UTK) • Paint and adhesion durability analysis (MSU) • Demonstrator part design, prototype production, and validation testing (Ford). The primary goal at the commencement of the project was development of a chopped carbon fiber sheet molding compound (SMC) that offered a three times improvement in tensile modulus over a comparable glass-based SMC. In addition to meeting mechanical performance targets, the resin kinetics were modified to achieve a processing cycle time of less than 3 minutes. Other critical-to-quality (CTQ) specifications were also stipulated to account for a broad range of materials and processing characteristics. To achieve the above, staff scientists at Dow Chemical created an extensive series of new epoxy blends for testing and validation. Throughout this development, a key challenge was attaining material performance goals without impacting processing behavior and paintability of finished components. The latter required a new internal mold release system being developed by Dow that was designed to complement the kinetics of the rapid cure epoxy. As a complement to work studies at the industrial partners, the teams from academia executed a series of analytical and experimental studies to investigate potential factors influencing CF-SMC performance. Unit cell models were developed to capture the meso-scale representations of the fiber matrix architecture. Results of this analysis and subsequent morphological investigations led to the design of a novel composite derivative comprising carbon fiber platelets embedded in an epoxy matrix; the platelet size and aspect ratio playing significant role in final composite properties. This approach was a departure from previous research in CF-SMC development whereby bulk filamentization or disassembly of the carbon fiber rovings had been considered the most effective means of achieving both fiber wet through and wet-out. As the course of the academia studies progressed, the aspect ratio of the fiber constituents was further optimized before finalizing material attributes and processing conditions. For the purposes of technology validation, the Ford team led a work stream devoted to the design, fabrication and testing of demonstration components. The carbon fiber SMC material has the potential to displace numerous stampings and castings on an automotive structure but ultimately vehicle closure applications were selected to showcase the abilities of the CF-SMC to achieve both mass reduction and business case for large complex structures. Using target properties established by the Dow staff scientists, the complete closure system for a full-size sedan decklid and a mid-size wagon liftgate were engineered. Prototypes for both applications were fabricated using production representative processing methods to allow for physical testing and performance validation of the CF-SMC structures. Following completion of a testing program that concluded with a FMVSS301 55 mph offset rear crash, the CF-SMC formulation was declared by the Ford team to have met all engineering requirements. To summarize, the joint development activities during this project led to significant technical breakthroughs and achievement of all milestones. The result was the development of a novel, tack-free carbon fiber molding compound that is suited to automated processing. This combined room temperature stability, fast cure kinetics, and internal mold release system facilitates cycle times that are conducive to high-volume production. The VORAFUSE M6400 successfully passed technology validation at Ford and is now eligible for consideration on future production commercial vehicle programs.

36 MATERIALS SCIENCE↗

Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems

Autonomous experimentation is an emerging area of research, primarily related to autonomous vehicles, scientific combinatorial discovery approaches in materials science and drug discovery, and iterative research loops of planning, experimentation, and analysis. However, autonomous approaches developed in these contexts are difficult to apply to high-dimensional mapping technologies, such as scanning hyperspectral imaging of biological systems, due to sample complexity and heterogeneity. We briefly cover the history of adaptive sampling algorithms and surrogate modeling in order to define autonomous adaptive data acquisition as an objective-based, flexible building block for future biological imaging experimentation driven by intelligent infrastructure. We subsequently summarize the recent implementations of autonomous adaptive data acquisition (AADA) for scanning hyperspectral imaging, assess how these address the difficulties of autonomous approaches in hyperspectral imaging, and highlight the AADA design variation from a goal-oriented perspective. Finally, we present a modular AADA architecture that embeds AADA-driven flexible building blocks to address the challenge of time resolution for high-dimensional scanning hyperspectral imaging of nonequilibrium dynamical systems. In our example research-driven experimental design case, we propose an AADA infrastructure for time-resolved, noninvasive, and label-free scanning hyperspectral imaging of living biological systems. This AADA infrastructure can accurately target the correct state of the system for experimental workflows that utilize subsequent expensive, high-information-content analytical techniques.

59 BASIC BIOLOGICAL SCIENCES↗

Effects of Photovoltaic Module Materials and Design on Module Deformation Under Load

Static structural finite element models of an aluminum-framed crystalline silicon (c-Si) photovoltaic (PV) module and a glass-glass thin film PV module were constructed and validated against experimental measurements of deflection under uniform pressure loading. Parametric analyses using Latin Hypercube Sampling (LHS) were performed to propagate simulation input uncertainties related to module material properties, dimensions, and manufacturing tolerances into expected uncertainties in simulated deflection predictions. This exercise verifies the applicability and validity of finite element modeling for predicting mechanical behavior of solar modules across architectures and enables computational models to be used with greater confidence in assessment of module mechanical stressors and design for reliability. Sensitivity analyses were also performed on the uncertainty quantification data sets using linear correlation coefficients to elucidate the key parameters influencing module deformation. This information has implications on which materials or parameters may be optimized to best increase module stiffness and reliability, whether the key optimization parameters change with module architecture or loading magnitudes, and whether parameters such as frame design and racking must be replicated in reduced-scale reliability studies to adequately capture full module mechanical behavior.

14 SOLAR ENERGY↗

Adaptive Cybersecurity for Distributed Energy Resources (AdCyDER): Online Reinforcement Learning with Stackelberg-Optimized Defenses — Pipeline Architecture, Evaluation Methodology, and Findings from a Synthetic-Data Evaluation

This report documents the design and evaluation of an integrated online-learning pipeline developed within the AdCyDER project for Distributed Energy Resource (DER) cybersecurity. The pipeline couples a Reinforcement Learning (RL) attack classifier — which produces an attack-type probability distribution — with a Stackelberg game-theoretic (GT) defense selector that consumes those distributions alongside SME-encoded priors over (defense, attack) effectiveness pairings and perdefense costs to choose grid-health-preserving defenses. The objective is not attack classification per se but production of distributions that drive effective defense selection through the Stackelberg layer, learned from delayed grid-health feedback rather than labeled attack data. AdCyDER as a whole is broader than the work presented here; this report covers the specific RL/GT loop integration and its evaluation. We present the integrated pipeline (SCADA telemetry with Fronius inverter physics, Suricata IDS, time-windowed aggregation, per-facility LSTM classifier, Stackelberg optimizer, OpenC2 actuators), an experimental campaign of 28 eight-hour iterations across three baseline modes, and a pipeline-ordered diagnostic protocol. The protocol identifies two distinct failure modes within the loop: paired supervised ceilings on the same features establish that the deployed online RL classifier (macro F1 ≈ 0.07) sits at least 4.7× below a same-architecture supervised LSTM (≈ 0.34) and 10–11× below a linear feature-signal ceiling (≈ 0.70–0.79 depending on per-facility isolation), localizing the dominant failure to the training procedure; and the reward signal driving online updates carries weak directional coupling with classifier correctness in the methodology-expected direction (multi-lens convergent: top-decile P(true) records produce more frequent state changes and slightly larger improvements, top-vs-bot Cohen’s 𝑑 ≈ −0.19), but at effect magnitudes too small to drive gradient-based learning at the campaign sample size. The original learning hypothesis is not supported by the data. The primary contributions are the diagnostic methodology — proposed as a transferable falsification protocol for online RL/GT defense pipelines learning from delayed environmental reward — and the open, reproducible experimental infrastructure. We outline reward reformulation as the highest-priority aspirational next step given the underpowered-but-aligned Q6 reading, with hardware-in-the-loop evaluation as the broadest scope-expansion option.

Blakely, Benjamin [Argonne National Laboratory (AN↗

Strangely attractive: Collaboration and feedback in the field of molecular magnetism

The fluid synergy between experimentalists and theoreticians has, for decades, led to a deeper understanding of the processes that govern single-molecule magnets. This approach has allowed for the establishment of proven design criteria for the control of magnetic properties through molecular architecture and the development of new magnetic measurement techniques and innovative computational methodologies. Here, we give an account of the experimental and theoretical joint endeavor carried out as part of the synthesis group led by David Mills and the computational/theoretical team led by Nicholas Chilton, together with colleagues in synthetic f-element chemistry and magnetism at the University of Manchester. In addition, we provide a personal perspective on collaborative work in molecular magnetism and how such collaborations are essential for advancing the field further.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗