Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “grid application”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Accuracy of the explicit energy-conserving particle-in-cell method for under-resolved simulations of capacitively coupled plasma discharges

The traditional explicit electrostatic momentum-conserving particle-in-cell algorithm requires strict resolution of the electron Debye length to deliver numerical stability and accuracy. The explicit electrostatic energy-conserving particle-in-cell algorithm alleviates this constraint with minimal modification to the traditional algorithm, retaining its simplicity, ease of parallelization, and acceleration on modern supercomputing architectures. In this article, we apply the algorithm to model a one-dimensional radio frequency capacitively coupled plasma discharge relevant to industrial applications. The energy-conserving approach closely matches the results from the momentum-conserving algorithm and retains accuracy even for cell sizes up to 8 times the electron Debye length. For even larger cells, the algorithm loses accuracy due to poor resolution of steep gradients within the radio frequency sheath. Accuracy can be recovered by adopting a non-uniform grid, which resolves the sheath and allows for cell sizes up to 32 times the electron Debye length in the quasi-neutral bulk of the discharge. The effect is an up to 8 times reduction in the number of required simulation cells, an improvement that can compound in higher-dimensional simulations. We therefore consider the explicit energy-conserving algorithm as a promising approach to significantly reduce the computational cost of full-scale device simulations and a pathway to delivering kinetic simulation capabilities of use to industry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Towards Feature-Preserving 2D and 3D Vector Field Compression

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type change in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications.

critical points↗
DOE OSTI2023

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗
DOE OSTI2024

gcm_eval (Global Climate Model Evaluation) [SWR-24-37]

The interplay between energy, climate, and weather is becoming more complex as our changing climate continues to affect the weather we experience which in turn drives changes in the ever increasing share of renewable energy generation and energy demand. Energy system analyses commonly rely on meteorological inputs to estimate renewable energy generation and energy demand; however, these inputs rarely represent the estimated impacts of future climate change. Climate models and publicly available climate change datasets can be used for this purpose, but the selection of inputs from the myriad of available models and datasets is a nuanced and often subjective process. There is no single perfect climate model or dataset for all applications. In this repository, we include software and the corresponding assessments of various global climate models (GCMs) from the Coupled Model Intercomparison Project (CMIP6), evaluating their skills with respect to the historical climate and comparing of their future projections of climate change. We focus on variables that directly affect the energy system including the representation of extreme values that can drive grid resilience events. The objective of this repository is not to recommend the best climate model and dataset for a given analysis, but instead to provide a reference to facilitate the selection of climate models and datasets in subsequent work.

Buster, Grant↗
DOE OSTI2020

Joint optimal scheduling for electric vehicle battery swapping-charging system based on wind farms

Insufficiencies in charging facilities limit the broad application of electric vehicles (EVs). In addition, EV can hardly represent a green option if its electricity primarily depends on fossil energy. Considering these two problems, this paper studies a battery swapping-charging system based on wind farms (hereinafter referred to as W-BSCS). In a W-BSCS, the wind farms not only supply electricity to the power grid but also cooperate with a centralized charge station (CCS), which can centrally charge EV batteries and then distribute them to multiple battery swapping stations (BSSs). The operational framework of the W-BSCS is analyzed, and some preprocessing technologies are developed to reduce complexity in modeling. Then, a joint optimal scheduling model involving a wind power generation plan, battery swapping demand, battery charging and discharging, and a vehicle routing problem (VRP) is established. Then a heuristic method based on the exhaustive search and the Genetic Algorithm is employed to solve the formulated NP-hard problem. Numerical results verify the effectiveness of the joint optimal scheduling model, and they also show that the W-BSCS has great potential to promote EVs and wind power.

17 WIND ENERGY↗
DOE OSTI2020

LA17-V-InverseRadTransportProblem-PD2Kb. Final Project Report

In the inverse radiation transport problem, a radiation signal emanating from an unknown object is used to reconstruct geometric and material parameters of the object , e.g., the mass of special nuclear material (SNM), the isotopic composition of the SNM (e.g., the enrichment of 235U), the thickness of shielding, etc. The inverse radiation transport problem is one of the most fundamental and important in the emergency response mission, and has been addressed in previous NA-22 and NA-42/84 investments by using derivative-free optimization methods. Our project was aimed at (i) consolidating previous work by applying polynomial chaos expansion (PCE) for reducing significantly the number of forward model simulations required by derivative-free optimization methods; (ii) developing a general-purpose framework and stand-alone software for solving the inverse radiation transport problem by applying a new “predictive modeling” methodology (predictive modeling of coupled multi-physics systems, PM_CMPS) which enables the fusing of experimental and computational information; and (iii) developing several benchmarks (for unscattered gamma rays) for demonstrating the quantification of non-Gaussian features (e.g., skewness and kurtosis, which characterize asymmetries and long tails) of response distributions by deterministically and efficiently propagating parameter uncertainties (covariances and higher-order moments, where available) using high-order sensitivities computed by the recently developed comprehensive adjoint sensitivity analysis methodology (C-ASAM). With regard to (i), application of PCE, the most significant accomplishment of this project was an implementation of PCE into INVERSE that significantly reduced computation time for the DiffeRential Evolution Adaptive Metropolis (DREAM) and Differential Evolution (DE) methods. For inverse transport problems with measurements of neutron leakage, neutron multiplication, and neutron-induced gamma-rays, the solution times for the DREAM and DE methods were reduced by factors of 30-300, reducing run times from hours to minutes while maintaining the same accuracy as the standard (non-PCE) DREAM and DE. Speedups were further improved by exploring sparse grid quadrature sets using the Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN (Tasmanian) toolset. An initial exploration of sparse grids indicated that further speedups factors of 5-10 are obtainable. The implementation of PCE in INVERSE is not in a form that users can easily access. With regard to (ii), solving the inverse problem using a predictive modeling paradigm, the most significant accomplishment of this project were the derivation and implementation of adjoint-based second derivatives in neutron and photon transport theory. There are two codes that compute these derivatives. SENSPG computes them for uncollided gamma rays and SENSMG computes them for neutron leakage. SENSMG is open-source code available on github. Other significant accomplishments included a stand-alone code, MULTI-PRED, that implements the predictive modeling paradigm, along with a detailed manual. The code was implemented in the existing INVERSE code for inverse problems, but a head-to-head comparison of inverse methods was not achieved. We also derived and implemented the capability to compute sensitivities for multiplicity measurements into the SENSMG code, and this capability was used in a data assimilation application, but it was not used for solving inverse problems. With regard to (iii), developing benchmarks to demonstrate the importance of high-order sensitivity and uncertainty analysis, the most significant accomplishment of this project were two test problems that showed how important second-order sensitivities are.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Demonstrating Advanced Nuclear Energy Solutions for Net Zero

Background/Objectives. The aggressive goals being set by nation states, communities, and private industry for decarbonization of grid electricity, industrial heat sources, and transportation around the world are imperative to mitigating the devastating effects that we are seeing from climate change. Although many of these goals focus on accomplishments by 2035 or 2050, the decisions that we make today won’t just impact the landscape of energy systems for the next 20 or 30 years—they will shape the world’s environment for centuries to come. That means that we can’t just focus on technologies that will get us to 2050, but technologies that will withstand our energy demands over that long-ranging future. Success will require us to utilize all of the clean energy resources that we have available to meet demands for electricity, heat, and steam, and we will need energy carriers such as hydrogen that do not emit additional greenhouse gases at the point of use. Nuclear energy, ranging from technologies in service today to advanced, higher temperature and modular systems that will be in service this decade, will provide a robust complement to renewable energy resources that operate variably. Researchers across the U.S. Department of Energy laboratory complex are working to advance multiple aspects of these clean energy solutions, with many focusing on integrated energy system solutions that leverage all available clean energy assets to meet wide-ranging energy demands. Approach/Activities. Nuclear energy is a proven, zero-emission option during operation that can provide consistent, dispatchable power to meet electricity demands while also providing high-quality heat that can meet energy demands beyond the electricity sector. Energy system design should seek to maximize these assets. As a dispatchable energy source with a small land utilization footprint, nuclear energy can be collocated with renewable resources, and the smaller systems that will be deployed this decade (ranging from a few megawatts to hundreds of megawatts) can be installed right where that energy is needed. Integrated nuclear and renewable systems will enhance power grid reliability and resilience, and they will help stabilize the grid through their increasingly flexible operation. Licensing, installation, and broad adoption of these advanced nuclear energy systems are expected to progress significantly in the 2020s, but this may be longer than desired by some stakeholders wishing to implement impactful clean energy decisions today. However, one must recall that nuclear energy systems will operate for 80 or more years, as is being demonstrated by current fleet nuclear systems. The nuclear community is extremely thorough in reviewing these systems with regard to safety and security; these efforts ensure that the deployed systems will continue to provide reliable, resilient energy over that operational lifetime. That investment of time up front will ensure that we can support energy demands over the centuries to come. While advanced nuclear technologies move through this process, communities and private industry may choose to install renewable generation systems that can later be coupled to the complementary nuclear systems as they become available—thus moving closer to the net zero goals in the near term. Choosing technologies and deployment configurations that allow small modular nuclear powerhouses to be added to these “energy parks” as they become available will ensure that advanced technologies can be readily adopted to support growing demands for clean energy. Results/Lessons Learned. The primary focus of integrated energy systems (IES) research is to assess the technical and economic potential of novel multi-input, multioutput solutions that are expected to enhance energy system flexibility, reliability, and resilience as we pursue a clean energy transition. Various energy applications and product streams beyond electricity are being evaluated, ranging from generation of potable water to production of hydrogen, fertilizers, synthetic fuels, and various chemicals. In early FY23 Idaho National Laboratory (INL) will commission thermal energy generation systems that emulate nuclear fission energy input using electric heating and will allow for integrated system testing with thermal energy storage, hydrogen production via high temperature electrolysis (HTE), and power systems hardware to demonstrate operation of a clean energy park within a microgrid or larger grid infrastructure, supporting up to 450 kW of heat input via electric heating and demonstrating operation of HTE systems at the multi-hundred kW scale. This presentation will highlight the wide array of RD&D being conducted at INL and partner laboratories to develop and deploy nuclear and renewable-based IES that will be key to achieving our net zero goals, including both computational and experimental demonstrations. By working with key collaborators in industry, analytical st

08 HYDROGEN↗
DOE OSTI2020

Corrosion resistance of functionally graded TiN/Ti coatings for proton exchange membrane fuel cells

Bipolar Plates (BPP) are important components of proton exchange membrane fuel cell (PEMFC) stacks. In the development of innovative fuel cell designs, it is advantageous to use aluminum for these applications, however, this material lacks the necessary corrosion resistance. Since the performance of PEMFC stacks depends on BPP properties, in particular, corrosion resistance, depositing titanium nitride (TiN) thin films onto aluminum substrates may improve their efficiency and durability. Present work focuses on improving corrosion resistance and hydrophobicity of TiN/Ti by using N graded films deposited onto aluminum substrates (AA-1100) by grid-assisted magnetron sputtering (GAMS). Electrochemical impedance spectroscopy (EIS) with potentiodynamic and potentiostatic polarization are used to investigate the performance of the substrate/film system at room temperature and 70 oC, thus simulating a prototypic PEMFC electrolyte environment. Electrochemical test results showed that graded TiN films improved corrosion resistance when compared with both the homogeneous films and the AA1100 uncoated substrate. Furthermore, contact angle results reveal improved hydrophobicity for both homogeneous and graded TiN coatings when compared with the AA1100 substrate.

25 ENERGY STORAGE↗
DOE OSTI2022

Automating Component-Level Stress Measurements for Inverter Reliability Estimation

In the near future, grid operators are expected to regularly use advanced distributed energy resource (DER) functions, defined in IEEE 1547-2018, to perform a range of grid-support operations. Many of these functions adjust the active and reactive power of the device through commanded or autonomous operating modes which induce new stresses on the power electronics components. In this work, an experimental and theoretical framework is introduced which couples laboratory-measured component stress with advanced inverter functionality and derives a reduction in useful lifetime based on an applicable reliability model. Multiple DER devices were instrumented to calculate the additional component stress under multiple reactive power setpoints to estimate associated DER lifetime reductions. A clear increase in switch loss was demonstrated as a function of irradiance level and power factor. This is replicated in the system-level efficiency measurements, although magnitudes were different—suggesting other loss mechanisms exist. Using an approximate Arrhenius thermal model for the switches, the experimental data indicate a lifetime reduction of 1.5% when operating the inverter at 0.85 PF—compared to unity PF—assuming the DER failure mechanism thermally driven within the H-bridge. If other failure mechanisms are discovered for a set of power electronics devices, this testing and calculation framework can easily be tailored to those failure mechanisms.

Flicker, Jack (ORCID:0000000212232154)↗
DOE OSTI2026

Thermal property characterization of phase change materials in building applications: A systematic review of fundamentals, recent progress, and future directions

Phase change materials (PCMs) can reduce building peak loads and enable demand-responsive thermal energy storage (TES), but their deployment depends on reliable measurement and interpretation of thermal properties across laboratory, intermediate, and application scales. Here, this review systematically examines characterization methods, testing protocols, and recent advances for neat PCMs and PCM composites, emphasizing thermal conductivity, enthalpy-related properties (phase change temperature, latent heat, specific heat), and cycling stability. For thermal conductivity, we compare steady-state and transient techniques and note limitations when phase transition and contact resistance affect measurements. For enthalpy–temperature characterization, we discuss differential scanning calorimetry together with intermediate- and bulk-scale methods, including T-history, heat flow meter testing, and three-layer calorimetry (3LC), to generate application-relevant enthalpy–temperature profiles. Cycling stability is organized into four experimental families: thermoelectric–air, fully thermoelectric, water-bath, and in situ chamber approaches, with attention to separating reversible supercooling from true degradation such as phase segregation. We highlight emerging noncontact diagnostics, including infrared thermography and embedded sensing, for spatially resolved validation and multiscale interpretation. Finally, we review the growing use of AI and machine learning for property prediction, inverse characterization from experimental signals, and real-time state estimation in building-integrated TES. Key needs include harmonized protocols, interlaboratory benchmarking, uncertainty reporting, and metadata-rich datasets to accelerate reproducible PCM qualification for grid-flexible buildings.

AI↗
DOE OSTI2021

A Study of Cost-Saving Potential of Load Flexibility Measures in Grid-Interactive Multifamily Buildings

With recent advances in smart technologies, more and more smart devices are penetrating the residential and commercial buildings market. The introduction of these smart devices is also helping IoT companies emerge with load aggregator roles in the sector. With more utility companies on the track of supporting OpenADR protocols, the aggregators could play a significant role in providing load flexibilities by automatically responding to demand response (DR) events and coordinating load flexibility measures between customers. This would benefit utility companies by reducing stress on the grid during critical peak demand hours as well as customers by allowing them to utilize utility rate structures advantageous to those able to reduce electric usage during high-demand hours. This study evaluates cost and energy savings from adopting multiple load flexibility measures in multifamily buildings. Combinations of different load flexibility measures, including space temperature floating, light dimming, automatic window shading, and water heater temperature floating, are considered. The simulations are performed using OpenStudio®, an open-source U.S. Department of Energy (DOE) simulation platform. For the case study, we used a midrise apartment building with weather conditions from Denver, Colorado. To compare climate zone differences, simulations were also performed for Los Angeles, California, and Chicago, Illinois. Initial results indicate that the application of automated load flexibility measures without careful consideration of dispatching strategies and DR program enrollments could significantly affect the savings. To get meaningful cost savings, aggregators need to encourage tenant awareness to curtail energy usage through occupant behavior in addition to dispatching automatic load flexibility measures. The outcomes from this study are believed to help load aggregators understand the risks and benefits of load flexibility opportunities.

building energy modeling↗
DOE OSTI2022

New large-strain FFT-based formulation and its application to model strain localization in nano-metallic laminates and other strongly anisotropic crystalline materials

This paper presents a new robust large-strain (LS) elasto-viscoplastic (EVP) formulation based on Fast Fourier Transforms (FFTs) for the prediction of the micro-mechanical response and microstructure evolution of polycrystalline and multiphase materials, with emphasis on the effect of strong crystallographic and/or morphologic anisotropy on localization of plastic deformation. In this work, the novel LS-EVPFFT formulation allows treatment of complex initial geometries and large deformations considering three grids of material points: a regular grid in the reference configuration, where FFTs can be performed; an irregular grid in the initial configuration, created by applying a stress-free displacement field to the reference regular grid; and an irregular grid in the current configuration, undergoing large strains and rotations as the material is loaded. Further numerical stability of the new formulation also required the use of a novel expression for the discrete modified Green’s operator, which reduces spurious field oscillations. After presenting and validating the new formulation by comparison with preexisting implementations and analytical solutions, LS-EVPFFT is applied to the prediction of slip and kink bands formation in polycrystalline columnar ice, and kink bands in single crystal zinc wires, showing good agreement with classic experiments. Finally, the model is used to study kink band formation during compression of Cu–Nb nano-metallic laminates (NMLs), in which accurate treatment of the complex geometry associated with the tortuosity of interfaces and large deformations become critical, showing consistency with corresponding micropillar experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Topology Optimization of Curved Electrodes with Suggested Simplified Corrugated Designs

The demand for space-efficient energy storage, from grid to device scale, has soared with the rapid electrification of society. To increase energy density while maintaining high rate performance, shaping electrodes has been shown to be an effective strategy. Computational optimization can suggest designs that improve energy storage performance; however, generated designs are often complex, leading to manufacturing challenges. Additionally, most optimization is typically conducted in standard rectangular domains, whereas some applications, such as in the aerospace industry or in consumer electronics, may benefit from optimized electrodes that fit in a nonstandard form factor. Here, in this work, we use topology optimization to design capacitive full-cell electrodes in a curved domain. We propose a simplified corrugated electrode design based on the optimization results and show that the energy stored in the simplified design is only reduced by about 5–27% when compared to that in the fully optimized design, with more deviation occurring when ionic transport in the porous electrode is particularly impeded. Overall, we find that these simplified designs are promising, particularly given the more conventional manufacturing techniques required, and have a 42–369% increase in energy stored compared to monolithic electrodes.

Energy - Storage↗
DOE OSTI2021

Segmented Energy Routing for a Modular AC/DC Hybrid System

This article presents a modular ac/dc system with both distributed and centralized power ports for energy router (ER) applications. In each module of the described system, photovoltaic (PV) power generation units, battery-type energy storage (ES) units, and critical loads are connected to the cascaded H-bridge (CHB)-organized medium-voltage (MV) dc links, with fully distributed low-voltage (LV) dc power ports. Copies of modules share the centralized load bus and interact with an MV ac grid in parallel. Hybrid power port (HPO) assigns flexibility to the system but makes energy routings a necessity for stable operation. In this article, a segmented energy management strategy for the HPO-ER is proposed. In terms of the grid-side power transferring, the system ratings are intentionally designed to match significant power imbalance. Focally, a segmented energy management strategy is proposed to realize fully autonomous energy routing involving MV ac grid, distributed PV generations, distributed battery storages, and LV load. The system is proven to be stable using the derived impedance-based model considering the interaction between power blocks. The feasibility of the topology and control strategy is also verified through software simulation, laboratorial hardware prototype experiment, and hardware-in-the-loop (HIL) emulation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Avista’s Shared Energy Economy Model Pilot: A Techno-economic Assessment

As part of the second round of the Washington Clean Energy Fund, Avista Corp received a $3.5 million matching grant in support of a shared energy economy project to test the integration of energy assets–from rooftop solar and battery storage to building energy management systems–that can be shared and used for multiple purposes. The goal of this project is to demonstrate how both the customer and the utility can benefit from this shared energy economy model and demonstrate that the electric grid can become more reliable, efficient, resilient, and flexible. Pacific Northwest National Laboratory was engaged by the U.S. Department of Energy and the Washington State Department of Commerce to work with Avista in assessing the benefits of the shared energy economy model. This report documents the techno-economic assessment of the shared energy economy model, including the definition of use cases and applications, collection and preparation of data and input parameters, development of modeling and optimization methods, and case studies and analysis results.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Thermal Experiments for Fractured Rock Characterization: Theoretical Analysis and Inverse Modeling

Abstract Field‐scale properties of fractured rocks play a crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion technique to infer two such parameters, fracture density and fractal dimension, from cross‐borehole thermal experiments data. It is based on a particle‐based heat‐transfer model, whose evaluation is accelerated with a deep neural network (DNN) surrogate that is integrated into a grid search. The DNN is trained on a small number of the heat‐transfer model runs and predicts the cumulative density function of the thermal field. The latter is used to compute fine posterior distributions of the (to be estimated) parameters. Our synthetic experiments reveal that fracture density is well constrained by data, while fractal dimension is harder to determine. Adding nonuniform prior information related to the DFN connectivity improves the inference of this parameter.

Zhou, Zitong↗
DOE OSTI2024

Improving Grid Awareness by Empowering Utilities with Machine Learning and Artificial Intelligence

Gap filling time series data typically depends on linear interpolation. More recently gap filling advancements include machine learning techniques. However, none leverage advanced learning approach that uses cohort training or a neighborhood informed approach, which is described in this report. The report also describes a physics informed approach using Reduced Order Models (ROM). There are several methods to capture the nature of the detailed system in aggregated models, however there is a trade-off for these methods developed for multiple applications. These methods have specific requirements and applications that includes consideration of dynamics or covering a larger range of operating conditions, etc. The various methods of aggregation are: 1) Thevenin equivalents for downstream networks 2) Equivalent feeder representation to capture downstream network losses accurately 3) Structured reduced order models for dynamics 4) System identification-based ROM (abstract dynamical model) Methods described in items 1 and 2 above are ideal for steady-state models and useful for this application. Of these two methods, based on the data availability, the targeted application, the reduced order model that is proposed to be developed is the equivalent feeder model representation. This includes a structure of the reduced order model whose parameters can be determined by the system load and losses with the meter measurements.

14 SOLAR ENERGY↗
DOE OSTI2020

Selected Negative Linear Compressibilities in the Metal–Organic Framework of [Cu(4,4'-bpy) 2 (H 2 O) 2 ]·SiF 6

Negative linear compressibility (NLC) is a rare high-pressure observation that lattice contraction is accompanied by the structural expansion along a specific direction. Generally, this counterintuitive phenomenon only derives from the intrinsic structural characteristics of materials and cannot be tuned by external perturbations. Searching for an effective method to control NLC effect is still a challenge in both chemical and material science. In this work, we successfully discover and select the NLC behaviors in the metal-organic framework (MOF) of [Cu(4,4'-bpy) 2 (H 2 O) 2 ]·SiF 6 (Cu(bpy)·SiF) through controlling the pressure conditions therein. The NLC effect of Cu(bpy)·SiF originates from the wine-rack mechanism that quasi-square grids transfer to rhombic ones with the structural expansion along the diagonal direction at high pressure. Meanwhile, both of the pressure range and magnitude of the NLC responses are enlarged with optimized pressure conditions. This study not only presents the intriguing selected NLC behaviors of a MOF but also proves the effects of pressure conditions on NLC, which offers promising strategies for further design and applications of NLC materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗