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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Efficient Solar Spectrum-Like White-Light Emission in Zinc-Based Zero-Dimensional Hybrid Metal Halides

Organic–inorganic metal halides (OIMHs) with high-efficiency solar spectrum-like emission are attracting broad and current interest. Here, in this work, five 0D Zn-based hybrid halides are synthesized based on aromatic organic cations with different carbon-chain lengths: C 6 H 5 CH 2 NH 3 + (PMA + ) and C 6 H 5 (CH 2 ) 4 NH 3 + (PBA + ). (PMA) 2 ZnCl 4 exhibits the highest photoluminescence quantum yield of 37.2% of reported Zn-based white-emission OIMHs. The emission spectrum of (PBA) 2 ZnI 4 indicates a color rendering index of 98, which is the highest among single-component white-light-emitting phosphors. Spectral characterizations and density functional theory calculations demonstrate that the extremely broad emission of (PBA) 2 ZnI 4 originates from the synergistic emission of organic cations and self-trapped excitons. The optical properties of the obtained (PMA) 2 ZnBr 4 , (PMA) 2 ZnI 4 ·H 2 O, and (PBA) 2 ZnCl 4 are also characterized for comparison, and with the same organic cations, the PLQY decreases from chloride to bromide to iodide. This work demonstrates that the selection of appropriate organics and halogens can enable fine tuning of single-component white-light emission, satisfying varying needs for solid-state lighting.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep Learning for Rapid Analysis of Spectroscopic Ellipsometry Data

High‐throughput experimental approaches to rapidly develop new materials require high‐throughput data analysis methods to match. Spectroscopic ellipsometry is a powerful method of optical properties characterization, but for unknown materials and/or layer structures the data analysis using traditional methods of nonlinear regression is too slow for autonomous, closed‐loop, high‐throughput experimentation. Herein, three methods (termed spectral, piecewise, and pointwise) of spectroscopic ellipsometry data analysis based on deep learning are introduced and studied. After initial training, the incremental time for inferring optical properties can be a thousand times faster than traditional methods. Results for multilayer sample structures with optically isotropic materials are presented, appropriate for high‐throughput studies of thin films of phase‐change materials such as GeSbTe (GST) alloys. Results for studies on highly birefringent layered materials are also presented, exemplified by the transition metal dichalcogenide MoS 2 . How the materials under test and the experimental objectives may guide the choice of analysis methods are discussed. The utility of our approach is demonstrated by analyzing data measured on a composition spread of GeSbTe phase‐change alloys containing 177 distinct compositions, and identifying the composition with optimal phase‐change figure of merit in only 1.4 s of analysis time.

Li, Yifei↗
DOE OSTI2022

Giant Orbital Anisotropy with Strong Spin–Orbit Coupling Established at the Pseudomorphic Interface of the Co/Pd Superlattice

Orbital anisotropy at interfaces in magnetic heterostructures has been key to pioneering spin–orbit-related phenomena. However, modulating the interface’s electronic structure to make it abnormally asymmetric has been challenging because of lack of appropriate methods. Here, the authors report that low-energy proton irradiation achieves a strong level of inversion asymmetry and unusual strain at interfaces in [Co/Pd] superlattices through nondestructive, selective removal of oxygen from Co3O4/Pd superlattices during irradiation. Structural investigations corroborate that progressive reduction of Co3O4into Co establishes pseudomorphic growth with sharp interfaces and atypically large tensile stress. The normal component of orbital to spin magnetic moment at the interface is the largest among those observed in layered Co systems, which is associated with giant orbital anisotropy theoretically confirmed, and resulting very large interfacial magnetic anisotropy is observed. All results attribute not only to giant orbital anisotropy but to enhanced interfacial spin–orbit coupling owing to the pseudomorphic nature at the interface. They are strongly supported by the observation of reversal of polarity of temperature-dependent Anomalous Hall signal, a signature of Berry phase. This work suggests that establishing both giant orbital anisotropy and strong spin–orbit coupling at the interface is key to exploring spintronic devices with new functionalities.

42 ENGINEERING↗
DOE OSTI2024

Crossbreeding Effect of Chalcogenation and Iodination on Benzene Additives Enables Optimized Morphology and 19.68% Efficiency of Organic Solar Cells

Volatile solid additives have attracted increasing attention in optimizing the morphology and improving the performance of currently dominated non-fullerene acceptor-based organic solar cells (OSCs). However, the underlying principles governing the rational design of volatile solid additives remain elusive. Herein, a series of efficient volatile solid additives are successfully developed by the crossbreeding effect of chalcogenation and iodination for optimizing the morphology and improving the photovoltaic performances of OSCs. Five benzene derivatives of 1,4-dimethoxybenzene (DOB), 1-iodo-4-methoxybenzene (OIB), 1-iodo-4-methylthiobenzene (SIB), 1,4-dimethylthiobenzene (DSB) and 1,4-diiodobenzene (DIB) are systematically studied, where the widely used DIB is used as the reference. The effect of chalcogenation and iodination on the overall property is comprehensively investigated, which indicates that the versatile functional groups provided various types of noncovalent interactions with the host materials for modulating the morphology. Among them, SIB with the combination of sulphuration and iodination enabled more appropriate interactions with the host blend, giving rise to a highly ordered molecular packing and more favorable morphology. As a result, the binary OSCs based on PM6:L8-BO and PBTz-F:L8-BO as well as the ternary OSCs based on PBTz-F:PM6:L8-BO achieved impressive high PCEs of 18.87%, 18.81% and 19.68%, respectively, which are among the highest values for OSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effect of Molecular Weight on the Morphology of a Polymer Semiconductor–Thermoplastic Elastomer Blend

Abstract Polymer semiconductors (PSCs) are essential active materials in mechanically stretchable electronic devices. However, many exhibit low fracture strain due to their rigid chain conformation and the presence of large crystalline domains. Here, a PSC/elastomer blend, poly[((2,6‐bis(thiophen‐2‐yl)‐3,7‐bis(9‐octylnonadecyl)thieno[3,2‐b]thieno[2′,3′:4,5]thieno[2,3‐d]thiophene)‐5,5′‐diyl)(2,5‐bis(8‐octyloctadecyl)‐3,6‐di(thiophen‐2‐yl)pyrrolo[3,4‐c]pyrrole‐1,4‐dione)‐5,5′‐diyl]] (P2TDPP2TFT4) and polystyrene‐ block ‐poly(ethylene‐ran‐butylene)‐ block ‐polystyrene (SEBS) are systematically investigated. Specifically, the effects of molecular weight of both SEBS and P2TDPP2TFT4 on the resulting blend morphology, mechanical, and electrical properties are explored. In addition to commonly used techniques, atomic force microscopy‐based nanomechanical images are used to provide additional insights into the blend film morphology. Opposing trends in SEBS‐induced aggregation are observed for the different P2TDPP2TFT4 molecular weights upon increasing the SEBS molecular weight from 87 to 276 kDa. Furthermore, these trends are seen in device performance trends for both molecular weights of P2TDPP2TFT4. SEBS molecular weight also has a substantial influence on the mesoscale phase separation. Strain at fracture increases dramatically upon blending, reaching a maximum value of 640% ± 20% in the blended films measured with film‐on‐water method. These results highlight the importance of molecular weight for electronic devices. In addition, this study provides valuable insights into appropriate polymer selections for stretchable semiconducting thin films that simultaneously possess excellent mechanical and electrical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Emulation of Synaptic Plasticity in WO 3 ‐Based Ion‐Gated Transistors

Neuromorphic systems, inspired by the human brain, promise significant advancements in computational efficiency and power consumption by integrating processing and memory functions, thereby addressing the von Neumann bottleneck. This paper explores the synaptic plasticity of a WO3-based ion-gated transistor (IGT) in [EMIM][TFSI] and a 0.1 mol L −1 LiTFSI in [EMIM][TFSI] for neuromorphic computing applications. Cyclic voltammetry (CV), transistor characteristics, and atomic force microscopy (AFM) force–distance (FD) profiling analyses reveal that Li + brings about ion intercalation, together with higher mobility and conductance, and slower response time (τ). WO 3 IGTs exhibit spike amplitude-dependent plasticity (SADP), spike number-dependent plasticity (SNDP), spike duration-dependent plasticity (SDDP), frequency-dependent plasticity (FDP), and paired-pulse facilitation (PPF), which are all crucial for mimicking biological synaptic functions and understanding how to achieve different types of plasticity in the same IGT. The findings underscore the importance of selecting the appropriate ionic medium to optimize the performance of synaptic transistors, enabling the development of neuromorphic systems capable of adaptive learning and real-time processing, which are essential for applications in artificial intelligence (AI).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Advanced Characterization Techniques for Overcoming Challenges of Perovskite Solar Cell Materials

Abstract In the last 10 years, organic–inorganic hybrid perovskite solar cells have achieved unprecedented advances, to the point where they now exhibit extremely high efficiency. However, long‐term stability and areal scalability limitations impede the commercial application of perovskite materials, and appropriate diagnosistic tools have become necessary to evaluate perovskite materials. Characterization of perovskite materials is regularly misinterpretated, due to unique intrinsic and extrinsic factors: degradation from the measurement source, ion migration, phase transition, and separation. Herein, studies on perovskites are reviewed that have used advanced characterization tools to overcome characterization challenges. Cryogenic temperature assisted measurements mitigate degradation or phase transitions induced by the measurement source. In situ measurements can track the variation of perovskite materials depending on external stimuli. Spatial material properties are able to be evaluated by the use of multidimensional mapping techniques. An overview of these advanced characterization tools that can overcome the challenges associated with established tools provides the opportunity for further understanding perovskite materials and solving the remaining challenges on the road to commercialization.

Kim, Min‐cheol↗
DOE OSTI2021

Laser-Irradiated Holey Graphene-Supported Single-Atom Catalyst towards Hydrogen Evolution and Oxygen Reduction

Single-atom catalysts (SAC) can boost the intrinsic catalytic activity of hydrogen evolution reaction (HER) and oxygen reduction reaction (ORR). However, the challenge remains due to the complex synthesis process and insufficient stability. A sustainable approach is applied to synthesizing SACs through laser irradiation and gaining mesoporous graphene oxide (MGO). The surface dangling bonds of nitrogen-doped MGO (NMGO) extract metal atoms species from Co or Fe metal foams and convert them to SAC via an appropriate synthesis approach. Notably, the Co-NMGO electrocatalyst requires low potentials of 146 mV to convey a current density of 10 mA cm -2 towards HER. Similarly, the Fe-NMGO electrocatalyst offers an onset of 0.79 V towards ORR in acidic solution. The individual metal atoms are confirmed via aberration-corrected scanning transmission electron microscopy, and X-ray absorption near-edge structure and extended X-ray absorption fine structure. Density functional theory calculations by applying the grand canonical potential kinetics model revealed that Co-NMGO shows the optimum free reaction energy of -0.17 eV at -0.1 V for HER, and Fe-NMGO has less limiting potential than that of Co-NMGO for ORR case. Furthermore, this work opens a new approach towards the synthesis of SAC and its mechanistic understandings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Mechanistic Elucidation of Electronically Conductive PEDOT:PSS Tailored Binder for a Potassium‐Ion Battery Graphite Anode: Electrochemical, Mechanical, and Thermal Safety Aspects

Potassium-ion batteries (KIBs) are considered more appropriate for grid-scale storage than lithium-ion batteries (LIBs) due to similar operating chemistry, abundant precursors, and compatibility with low-cost graphite anodes. However, a larger ion reduces rate capabilities and exacerbates capacity fading from volumetric expansion. In this report, conductive polymer, poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), is substituted for standard insulating polyvinylidene fluoride (PVDF). Half-cells using carbon black (CB) in continuously conductive PEDOT:PSS/CB binder outperforms PVDF/CB by mitigating electrically isolated “dead” graphite, improving 100 cycle capacity retention at C/10 from 63 to 80%. Enhanced electrical contact with PEDOT:PSS/CB also reduces ion impedance and improves rate capabilities. Without CB however, PEDOT:PSS binder performs poorly in electrochemical studies despite promising ex situ electronic conductivity. This discrepancy is mechanistically elucidated through identification of redox activity between PEDOT:PSS and K + which results in high impedances in the anode operating voltage window. Additionally, the impact of conducting binder on mechanical properties and thermal safety of the anode is investigated. Brittleness and poor wettability of PEDOT:PSS are identified as issues, but greater stability against reactive KC 8 reduces overall heat generation. Binder substitution offers a promising means of mitigating issues with current KIB anodes regardless of active material, and the work herein addresses issues towards further improvement.

electrochemical impedance spectroscopy↗
DOE OSTI2022

Thermodynamically Driven Synthetic Optimization for Cation-Disordered Rock Salt Cathodes

Relating the synthesis conditions of materials to their functional performance has long been an experience-based trial-and-error process. However, this methodology is not always efficient in identifying an appropriate protocol and can lead to overlooked opportunities for the performance optimization of materials through simple modifications of the synthesis process. In this work, we systematically track the structural evolution in the synthesis of a representative disordered rocksalt (a promising next-generation Li-ion cathode material) at the scale of both the long-range crystal structure and the short-range atomic structure using various in situ and ex situ techniques, including transmission electron microscopy, X-ray diffraction, and pair distribution function analysis. Further, an optimization strategy is proposed for the synthesis protocol, leading to a remarkably enhanced capacity (specific energy) of 313 mAh g –1 (987 Wh kg –1 ) at a low rate (20 mA g –1 ), with a capacity of more than 140 mAh g –1 retained even at a very high cycling rate of 2,000 mA g –1 . This strategy is further rationalized using ab initio calculations, and important opportunities for synthetic optimization demonstrated in this study are highlighted.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Insight of Synthesis of Single Crystal Ni-Rich LiNi 1-x-y Co x Mn y O 2 Cathodes

Single-crystal Ni-rich LiNi 1-x-y Co x Mn y O 2 (NCM) cathodes have garnered widespread attention in the lithium-ion battery community due to their unique advantages in mechanical performance and their ability to minimize interfacial electrochemical side reactions. The synthesis of single-crystal materials with monodisperse and appropriate size, minimal lattice defects, and highly ordered structures is the key for high-performance batteries. However, achieving this goal poses challenges due to the lack of in-depth understanding regarding specific experimental parameters and the solid reaction mechanism during the synthesis process. Further, in this review, the aim is to provide an in-depth analysis of the critical process parameters involved in the synthesis and their impact on crystal morphology, structure, and electrochemical performance. Consequently, the first section focuses on the effect of the precursor morphology, lithium salt, atmosphere, and sintering procedure. In the second section, the study delves into an in-depth discussion of the solid reaction and crystal growth mechanism. Lastly, it is concluded by highlighting the prospects and challenges associated with the synthesis and application of single-crystal Ni-rich NCM cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Linear relationships for modeling CO 2 absorption in aqueous alkanolamine solutions in a thermodynamically consistent way

A thermodynamically consistent model for the carbon dioxide (CO 2 ) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO 2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well-known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. Here, the proposed relationships have been applied to the development of an electrolyte nonrandom two liquid (NRTL) activity coefficient model for CO 2 absorption in aqueous 1-amino-2-propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor–liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A Roadmap for Reaching the Potential of Brain-Derived Computing

Neuromorphic computing is a critical future technology for the computing industry, but it has yet to achieve its promise and has struggled to establish a cohesive research community. A large part of the challenge is that full realization of the potential of brain inspiration requires advances in both device hardware, computing architectures, and algorithms. This simultaneous development across technology scales is unprecedented in the computing field. This article presents a strategy, framed by market and policy pressures, for moving past these current technological and cultural hurdles to realize its full impact across technology. Achieving the full potential of brain-derived algorithms as well as post-complementary metal-oxide-semiconductor (CMOS) scaling neuromorphic hardware requires appropriately balancing the near-term opportunities of deep learning applications with the long-term potential of less understood opportunities in neural computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Experimental validation of multiphysics model simulations of the thermal response of a cement clinker rotary kiln at laboratory scale

Abstract An increasing demand for buildings, transportation systems and civil infrastructure development has driven expansion of cement consumption world‐wide, producing a significant increase in related global energy demand. With approximately 7% of the world‐wide industrial energy consumption (10.7 exajoules [EJ]), the cement industry is the third most energy intensive industrial processes and a key component for concrete, the most consumed composite material in the global construction industry. In cement manufacturing, the cement kiln accounts for most of the energy consumption in the production process. As the heart of a cement plant, the cement kiln is where the kiln feed primarily containing calcium oxide (CaO), silica (SiO 2 ), alumina (Al 2 O 3 ), and iron (Fe 2 O 3 ) are thermally and chemically transformed into clinker minerals. The presented work developed a multiphysics model, designed and built a laboratory‐scale rotary cement clinker kiln, and produced cement clinker at laboratory‐scale. The model was developed to study the interaction between the various thermal, fluid dynamic and chemical interactions involved in the sintering process used to form Portland cement clinker in an effort to reduce energy use. The analytical model was validated through experimental testing using a unique laboratory‐scale rotary cement kiln developed during the investigation. Also demonstrated was the feasibility of producing clinker at laboratory scale. This modeling and lab scale tests were designed to better understand the clinker sintering process so that operational and quality decisions can be made to optimize energy consumption without compromising cement clinker quality. The computational fluid dynamics modeling was developed in COMSOL Multiphysics 6.0. The characteristics of the combustion fluid flow, concentration of species, temperature and heat transfer were studied for a turbulent flow of methane (CH 4 ) gas and oxygen (O 2 ). Theory suggests that heat transfer impacts the cement production process but the multiphysics model more accurately describes the convection, conduction, and radiant heat transfer in the kilning process and thus allows for a better understanding of the energy exchange driving the chemical reactions that produce Portland cement. Clinker minerals were formed because of appropriate burning conditions implemented during experimental model validation.

Tabares, Juan David↗
DOE OSTI2022

A simulation‐based integrated virtual testbed for dynamic optimization in smart manufacturing systems

Abstract In a manufacturing system, production control‐related decision‐making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation‐based virtual testbed that implements dynamic optimization, automatic information exchange, and decision‐making from the process‐level, system‐level, and factory‐level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python‐based numerical computation program, discrete‐event simulation software (Simul8), and an optimization solver (CPLEX) via a third‐party master program. The application of this simulation‐based virtual testbed is illustrated by a case study in a machining shop.

Sun, Yuting↗
DOE OSTI2023

A review of artificial intelligence applications in manufacturing operations

Abstract Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

Plathottam, Siby Jose↗
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
DOE OSTI2020

Structural Diversity and Magnetic Properties of Hybrid Ruthenium Halide Perovskites and Related Compounds

Abstract There has been a great deal of recent interest in extended compounds containing Ru 3+ and Ru 4+ in light of their range of unusual physical properties. Many of these properties are displayed in compounds with the perovskite and related structures. Here we report an array of structurally diverse hybrid ruthenium halide perovskites and related compounds: MA 2 Ru X 6 ( X =Cl or Br), MA 2 M RuX 6 ( M =Na, K or Ag; X =Cl or Br) and MA 3 Ru 2 X 9 ( X =Br) based upon the use of methylammonium (MA=CH 3 NH 3 + ) on the perovskite A site. The compounds MA 2 Ru X 6 with Ru 4+ crystallize in the trigonal space group and can be described as vacancy‐ordered double‐perovskites. The ordered compounds MA 2 M Ru X 6 with M + and Ru 3+ crystallize in a structure related to BaNiO 3 with alternating MX 6 and Ru X 6 face‐shared octahedra forming linear chains in the trigonal space group. The compound MA 3 Ru 2 Br 9 crystallizes in the orthorhombic Cmcm space group and displays pairs of face‐sharing octahedra forming isolated Ru 2 Br 9 moieties with very short Ru–Ru contacts of 2.789 Å. The structural details, including the role of hydrogen bonding and dimensionality, as well as the optical and magnetic properties of these compounds are described. The magnetic behavior of all three classes of compounds is influenced by spin–orbit coupling and their temperature‐dependent behavior has been compared with the predictions of the appropriate Kotani models.

Vishnoi, Pratap↗