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At least 1,405 records · Page 78

Electrochemical Mitigation of Corrosion in Molten Chloride Salts During CSP Plant Operation

We are designing an electrochemical flow-cell for removal of corrosive impurities from molten chloride salt Gen3 Concentrating Solar Power (CSP) plants during plant operation. Corrosive impurities will inevitably form in molten chloride salts upon exposure to air and moisture. We previously showed that even small amounts of these impurities, especially MgOHCl, will be detrimental in Gen3 CSP plants, necessitating prohibitively expensive containment alloys and frequent replacement of corroded components. Pre-purification of salt with Mg metal at temperatures above 650 degrees C is the current method for removing corrosive impurities from chloride salts before they are introduced to CSP systems. However, this is not a suitable method for impurity removal during plant operation. First, this method will produce MgO particulates which will damage plant components. Second, Mg metal is solid at the low temperature point (500 degrees C), so the purification will not proceed at a fast rate. At the high temperature point, Mg is soluble. In this case, fast purification may proceed, but dissolved metal is likely to precipitate out in cold-temperature point components, causing damage. In contrast, our electrochemically driven method allows fast Mg-based purification to proceed at the low temperature point, without formation of harmful particulates and without the risk of Mg metal precipitation. This novel approach is inspired by electrorefining techniques that are widely employed in industrial metallurgy for removal of impurities from metals. Impurities in the incoming molten salt will be reduced to inert MgO at the cathode, which can be removed by periodically washing the cell with acid. Simultaneously, Mg dissolution at the anode will ensure salt composition is maintained, with no net removal of Mg2+. We have validated this electrochemical approach at lab scale under static conditions with batch rectors. Furthermore, we have performed analytical modeling and technoeconomic analysis to produce a preliminary engineering design for the purification flow cell.

CSP↗

Use of carbonyldiimidazole as a derivatization agent for the detection of pinacolyl alcohol, a forensic marker for Soman, by EI-GC–MS and LC-HRMS in official OPCW proficiency test matrices

Pinacolyl alcohol (PA), a key forensic marker for the nerve agent Soman (GD), is a particularly difficult analyte to detect by various analytical methods. In this work, we have explored the reaction between PA and 1,1'-carbonyldiimidazole (CDI) to yield pinacolyl 1H-imidazole-1-carboxylate (PIC), a product that can be conveniently detected by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography-high-resolution mass spectrometry (LC-HRMS). Regarding its GC–MS profile, this new carbamate derivative of PA possesses favorable chromatographic features such as a sharp peak and a longer retention time (RT = 16.62 min) relative to PA (broad peak and short retention time, RT = 4.1 min). Additionally, the derivative can also be detected by LC-HRMS, providing an avenue for the analysis of this chemical using this technique where PA is virtually undetectable unless present in large concentrations. From a forensic science standpoint, detection of this low molecular weight alcohol signals the past or latent presence of the nerve agent Soman (GD) in a given matrix (i.e., environmental or biological). The efficiency of the protocol was tested separately in the analysis and detection of PA by EI-GC–MS and LC-HRMS when present at a 10 μg/mL in a soil matrix featured in the 44th PT and in a glycerol-rich liquid matrix featured in the 48th Official Organization for the Prohibition of Chemical Weapons (OPCW) Proficiency Test when present at a 5 μg/mL concentration. In both scenarios, PA was successfully transformed into PIC, establishing the protocol as an additional tool for the analysis of this unnatural and unique nerve agent marker by GC–MS and LC-HRMS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Moving from Fundamental Knowledge of Kinetics and Mechanisms on Surfaces to Prediction of Catalyst Performance in Reactors

Catalysis is defined by kinetics, but after over a century of catalyst development our kinetic and mechanistic knowledge governing real-world catalysis is still severely lacking. This Perspective considers how we can use the precise knowledge available from both theoretical and fundamental experimental techniques to understand and even predict catalytic performance under operational conditions. Here, we describe advances that link fundamental and "real-world" measurements, crucial elements required to successfully recreate a reaction network, and avenues which appear to offer viable routes to success.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Opportunities and Limitations of Nuclear Magnetic Resonance Spectroscopy in Astrobiology

For decades, Nuclear Magnetic Resonance (NMR) spectroscopy has been utilized as a powerful tool in various scientific disciplines, most prominently in chemistry, to determine molecular structures or monitor reactions. While well established in various fields, NMR applications in astrobiology are still unclear. This work aims to explore the potential of NMR in astrobiology, highlighting strengths but also weaknesses. We illustrate capabilities of NMR with two applications: (1) recently developed methods for position-specific carbon isotope analysis of complex organics; and (2) well-established tools for performing quantitative compositional analysis of complex organic mixtures. By utilizing samples relevant to astrobiology, specifically the amino acid valine and analogue mixtures of organics, we showcase that molecules retain a source dependent and distinct intramolecular carbon isotope fingerprint. We demonstrate that compositional sample analysis provides an independent and complementary line of evidence pointing towards the origin of a molecule or mixture. Together, these NMR tools have the potential to support life detection efforts, and aid in distinguishing between biotic and abiotic samples. Finally, we discuss sensitivity, detection limits, the portability of NMR, and propose how integration with mass-spectrometry techniques will be imperative to enable more targeted and comprehensive analyses relevant to astrobiology, including in-situ analysis but also sample return missions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Direct visualization of radiation-induced transformations at alkali halide–air interfaces

Radiation driven reactions at mineral/air interfaces are important to the chemistry of the atmosphere, but experimental constraints (e.g. simultaneous irradiation, in situ observation, and environmental control) leave process understanding incomplete. Using a custom atomic force microscope equipped with an integrated X-ray source, transformation of potassium bromide surfaces to potassium nitrate by air radiolysis species was followed directly in situ at the nanoscale. Radiolysis initiates dynamic step edge dissolution, surface composition evolution, and ultimately nucleation and heteroepitaxial growth of potassium nitrate crystallites mediated by surface diffusion at rates controlled by adsorbed water. Finally, in contrast to in situ electron microscopy and synchrotron-based imaging techniques where high radiation doses are intrinsic, our approach illustrates the value of decoupling irradiation and the basis of observation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Calculation of the detonation state of HN 3 with quantum accuracy

HN 3 is a unique liquid energetic material that exhibits ultrafast detonation chemistry and a transition to metallic states during detonation. We combine the Chebyshev interaction model for efficient simulation (ChIMES) many-body reactive force field and the extended-Lagrangian multiscale shock technique molecular dynamics method to calculate the detonation properties of HN 3 with the accuracy of Kohn–Sham density-functional theory. ChIMES is based on a Chebyshev polynomial expansion and can accurately reproduce density-functional theory molecular dynamics (DFT-MD) simulations for a wide range of unreactive and decomposition conditions of liquid HN 3 . We show that addition of random displacement configurations and the energies of gas-phase equilibrium products in the training set allows ChIMES to efficiently explore the complex potential energy surface. Schemes for selecting force field parameters and the inclusion of stress tensor and energy data in the training set are examined. Structural and dynamical properties and chemistry predictions for the resulting models are benchmarked against DFT-MD. We demonstrate that the inclusion of explicit four-body energy terms is necessary to capture the potential energy surface across a wide range of conditions. Our results generally retain the accuracy of DFT-MD while yielding a high degree of computational efficiency, allowing simulations to approach orders of magnitude larger time and spatial scales. The techniques and recipes for MD model creation we present allow for direct simulation of nanosecond shock compression experiments and calculation of the detonation properties of materials with the accuracy of Kohn–Sham density-functional theory.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Theory and application of the vector pair correlation function for real-space crystallographic analysis of order/disorder correlations from STEM images

Deviations of local structure and chemistry from the average crystalline unit cell are increasingly recognized to have a significant influence on the properties of many technologically important materials. Here, we present the vector pair correlation function (vPCF) as a new real-space crystallographic analysis method, which can be applied to atomic-resolution scanning transmission electron microscopy (STEM) images to quantify and analyze structural order/disorder correlations. Our STEM-based vPCFs have several advantages over radial PCFs and/or 3D pair distribution functions from x-ray total scattering: vPCFs explicitly retain crystallographic orientation information, are spatially resolved, can be applied directly on a sublattice basis, and are suitable for any material that can be imaged with STEM. To show the utility of our approach, we measure partial vPCFs in Ba 5 SmSn 3 Nb 7 O 30 (BSSN), a tetragonal tungsten bronze (TTB) structured complex oxide. Many TTBs are known to be classical or relaxor ferroelectrics, and these properties have been correlated with the presence of superlattice ordering. BSSN, specifically, exhibits relaxor behavior and an incommensurate structural modulation. From the vPCF data, we observe that, of the cation sites, only the Ba (A2) sublattice is structurally modulated. We then infer the local modulation vector and reveal a marked anisotropy in its correlation length. Finally, short-range correlated polar displacements on the B2 cation sites are observed. This work introduces the vPCF as a powerful real-space crystallography technique, which enables direct, robust quantification of short-to-long range order on a sublattice-specific basis and is applicable to a wide range of complex material types.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Thermomechanical Degradation of Sintered Copper Under High-Temperature Thermal Shock

The need for reliable bonded interface materials is critical to realize the performance benefits of wide-bandgap devices in power electronics modules, especially in operating temperatures greater than 150 degrees C. In this paper, we investigate the thermomechanical performance of sintered copper (Cu) as a large-area attachment, bonded between Cu baseplates and active-metal-bonded substrates, under accelerated thermal shock (-40 degrees C to 200 degrees C) conditions. In the fabrication phase of the samples, we used different stencil patterns and found that the grid and stripe patterns resulted in a better outgassing of the residual organics during the sintering process, thereby ensuring a substantially improved bond quality compared to a full-area print. The paste consisted of Cu microflakes, and we performed sintering using a Budatec SP300 sintering press at 275 degrees C with 15 MPa of bonding pressure for 5 minutes in a nitrogen atmosphere. Under accelerated experiments, we monitored the degradation of the sintered Cu bond in the samples through C-mode scanning acoustic microscope (C-SAM) images. To quantify the defect percentage in C-SAM images, we investigated image denoising techniques to exclude the pattern prints. Finally, we cross-sectioned a sample and obtained scanning electron microscope images, which revealed adhesive fracture as the dominant failure mechanism.

ADVANCED PROPULSION SYSTEMS,INORGANIC, ORGANIC, PH↗

Tuning Organic Semiconductor Packing and Morphology through Non-equilibrium Solution Processing (Final Report)

Organic semiconductors (OSCs) are a promising candidate to produce low-cost, large area, and flexible electronics. There has previously been successful development of organic field effect transistors (OFETs), photovoltaics (OPV), and bioelectronics using OSCs. Solution processing of these materials allows for the fabrication of large-area devices in the kinetic crystallization regime. The charge transport capabilities have been shown to depend on the morphology and molecular packing of the OSC within thin films. Our hypothesis is that solution processing conditions may significantly impact OSC morphology and as a result the charge transport. Therefore, this work has focused on obtaining a fundamental understanding of the morphology and molecular packing of OSCs for optimal device performance. Through our work, we have gained a better understanding of how these structural conditions were influenced by the solution processing conditions. Our studies have led to a more systematic understanding of the various parameters that impact OSC morphology. Solution processing leads to non-equilibrium films, thus allowing for the formation of diverse morphologies that are inaccessible by other fabrication methods. Previously, our group has focused on tuning the morphology of small-molecular OSCs [53, 55-57]. However, little work had been devoted to polymer OSCs. There was a lack of detailed studies characterizing the solution-state of polymer OSCs in terms of their conformation, degree of entanglement, polymer aggregation, and chain relaxation dynamics. The solution state properties are also likely to be influenced by the rigidity and molecular weight of the polymer OSCs investigated. Thus, we have focused our attention on gaining a better insight of polymer OSC films and solution processing methods. Through this proposal, our approach is to investigate the correlation between solution-state properties and the morphology of the resulting polymer OSC films. We worked three specific aims: investigate the effects of 1) polymer OSC solution-state properties on final film morphology; 2) molecular additives on the solution-state and final film properties; 3) controlled pre-aggregation in the solution-state in the final film morphology. More rigid and planar polymer backbones should promote interchain charge transport and more efficient interchain hopping between polymer chains. Through tailoring the polymer backbone, polymer sidechains, and molecular weight, we expected the altered solution-state properties to affect the final film morphology. In addition, reducing the entanglements and promoting chain alignment will likely prevent charge carrier trapping through conformation disorders. We thus studied different mechanisms, such as molecular additives, to reduce entanglements in solution. Devices fabricated from these solutions were expected to have improved charge transport abilities. In addition to tailoring the solution-state characteristics of the polymer OSCs, we investigated the effect of solution processing methods on the molecular packing and morphology of polymer OSC films. Two main solution processing techniques, e.g., spin-coating and solution shearing, were employed to fabricate OFETs. Spin coating was employed to prepare OSC films, which produce isotropic films. This technique creates several parameters to tune such as spin coating speed, acceleration, and time. Additionally, our research group developed the solution shearing method, which consists of the solution initially sandwiched between two plates. By sliding the top plate, the solution front is exposed and drying begins. This technique allows for the creation of aligned large crystalline domains. Solution shearing consists of different processing parameters which can affect the final morphology, such as shearing speed, substrate temperature and temperature gradient, distance between both plates, and the tilt angle of the top plate. Due to the complexity of the polymer systems investigated, numerous techniques were employed to characterize the polymer OSC solution state and films. All the materials were characterized using the DOE supported synchrotron X-ray scattering facilities at the Stanford Synchrotron Radiation Lightsource (SSRL). Using grazing incidence X-ray scattering (GIXS) and Near Edge X-ray Absorption Fine Structure (NEXAFS) techniques, the crystalline structure and molecular orientations of the thin films were measured. Optical absorption (UV-Vis) spectroscopy was employed to determine the aggregation state in solution and films of the polymer systems. Polarized UV-Vis also allowed for the determination of the relative degree of polymer chain alignment for solution sheared films. Additionally, various other techniques were used to investigate other properties within the film, such as atomic force microscopy (AFM) and solution rheology. Finally, the device performance is quantified through the fabrication and characterization of OFETs, which will highlight the effects of morphology on charge transport.

36 MATERIALS SCIENCE↗

Mathematical Tools for the Analysis of Periodic and Aperiodic Grid Signals

Numerous sensing techniques and instrumentation are used in determining the state of the electric grid. Coupled with such measurements are the methods and analysis tools used. The parameters of interest result in both periodic and aperiodic signals. An examination of appropriate analysis tools is presented

97 MATHEMATICS AND COMPUTING↗

Multiscale spatial analysis of fracture arrangement and pattern reconstruction using Ripley's K-function

This work presents novel multiscale spatial data analytics using Ripley's K-function, as a measure of spatial interaction, to study one-dimensional arrangement of fractures. Fracture spatial arrangements are classified into clustered, anticlustered, or indistinguishable from random by testing statistical significance of the calculated Ripley's K-function. Characterizations of fracture arrangements are performed as a function of length scale and position. Analysis of the K-function along the study interval identifies where fracture clustering and anticlustering occur. Here, a simulation technique is also introduced here to statistically reconstruct spatial arrangements and to generate fracture realizations that are spatially similar to the fractures observed in the field. With this simulation technique, one can also fill spatial gaps in fracture measurements where data are absent, unreliable, or unused. Synthetic as well as field-measured 1D fracture datasets are used for testing and demonstration. Methods introduced in this work can be readily applied to fracture datasets observed in outcrops, borehole image logs, and cores.

58 GEOSCIENCES↗

Nanoscale Perspectives of Metal Degradation via In Situ Atom Probe Tomography

Abstract We report a unique in situ instrument development effort dedicated to studying gas/solid interactions relevant to heterogeneous catalysis and early stages of oxidation of materials via atom probe tomography and microscopy (APM). An in situ reactor cell, similar in concept to other reports, has been developed to expose nanoscale volumes of material to reactive gas environments, in which temperature, pressure, and gas chemistry are well controlled. We demonstrate that the combination of this reactor cell with APM techniques can aid in building a better mechanistic understanding of resultant composition and surface and subsurface structure changes accompanying gas/surface reactions in metal and metal alloy systems through a series of case studies: O 2 /Rh, O 2 /Co, and O 2 /Zircaloy-4. In addition, the basis of a novel operando mode of analysis within an atom probe instrument is also reported. The work presented here supports the implementation of APM techniques dedicated to atomic to near-atomically resolved gas/surface interaction studies of materials broadly relevant to heterogeneous catalysis and oxidation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Computational study of the electrostatic potential and charges of multivalent ionic liquid molecules

The electrostatic interactions in ionic liquids (ILs) can be difficult to quantify, but this information is very valuable for improving intermolecular potentials for performing molecular-level simulations. Although polarizable forcefields can provide improved accuracy, the computational efficiency of fixed charge molecular models is often preferred for larger systems. Here, we propose a multi-scale screening approach for analyzing the charge scaling behavior and other electrostatic properties of ILs, which is more attainable than previous analysis methods that rely on the IL crystalline structures. Based on isolated molecules and small clusters, we study the electrostatic potential properties and charges of multivalent IL molecules using density-functional theory calculations and ab initio molecular dynamics simulations. The charges of the IL molecules are calculated and compared using several different charge estimation techniques. Although there are some differences among the estimated partial charges on the atoms, the total anion and cation charges are very similar. In conclusion, the charge scaling factors are substantial, and they are only slightly affected by the IL cluster size model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

How Collective Phenomena Impact CO 2 Reactivity and Speciation in Different Media

CO 2 has attracted considerable attention in the recent years due to its role in the greenhouse effect and environmental management. While its reaction with water has been studied extensively, the same cannot be said for reactivity in supercritical CO 2 phase, where the conjugate acid/base equilibria proceed through different mechanisms and activation barriers. Furthermore, in spite of the apparent simplicity of the CO 2 + H 2 O reaction, the collective effect of different environments has drastic influence on the free energy profile. Enhanced sampling techniques and well-tailored collective variables provide a detailed picture of the enthalpic and entropic drivers underscoring the differences in the formation mechanism of carbonic acid in the gas, aqueous and supercritical CO 2 phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Photoinitiated thermoset polymerization through controlled release of metathesis catalysts encapsulated in poly(phthalaldehyde)

Photoinitiated polymerization enables spatiotemporal control of reaction conditions and can thereby generate materials with high complexity while consuming minimal energy. Where ring opening metathesis polymerization (ROMP) is concerned, photo-activated processes are typically enabled by chemical inhibition of ruthenium carbenes via the careful design of complexed ligands such that photoactivation can proceed through an isomerization or ligand dissociation event. In this contribution, we have explored a new approach to photoinitiation of ROMP based on physical inhibition through microencapsulation and controlled release of metathesis catalysts. Micron-sized particles of poly(phthalaldehyde) (PPA), catalyst, and photoacid generator were fabricated by spray drying. The particles were dispersed in dicyclopentadiene monomer, after which polymerization was initiated through temperature or UV exposure, both inducing depolymerization of the PPA particles and in situ catalyst release. The monomer/particle dispersions were found to be stable and reproducibly polymerizable with 3 weeks of storage at room temperature. Furthermore, the dispersions can be used for both photo- and thermal-initiated frontal ROMP, yielding a polymerized thermoset of equivalent properties to conventional bulk- and frontally-polymerized analogues. In conclusion, this work will ultimately enable new manufacturing techniques for ROMP-based materials, due to the modular, easily tunable nature of the underlying initiating system and its unparalleled stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Waveform processing using neural network algorithms on the front-end electronics

In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as “edge computing”. Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. Further, to better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.

47 OTHER INSTRUMENTATION↗

Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S.

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

59 BASIC BIOLOGICAL SCIENCES↗