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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Analysis of the laser welding keyhole using inline coherent imaging

Laser beam welding is a widely used fusion welding process in many industrial applications such as automotive, aerospace, energy, defense, and medical products. Industry has a fundamental need to model the laser welding process to minimize experimental testing and improve confidence in production welds. However, computational models attempting to predict weld formation are limited by an incomplete understanding of the beam-material interactions. As a consequence, these models do not accurately predict the mechanisms associated with laser weld formation. To improve the current state of prediction capabilities, it is vital to better detect/measure the physical aspects of the weld pool during high energy density welding. In this work, a novel real-time laser weld monitoring device using inline coherent imaging (ICI) was used to provide a fundamental understanding of laser weld formation via vaporization. The objective of this work was to investigate and quantify the relationship relating laser weld parameters and the vapor capillary (keyhole) through a state-of-the-art measurement technique. Bead-on-plate laser beam welds were produced with partial penetration on 304L stainless steel, 2205 duplex stainless steel, and Ti-6Al-4V. Keyhole monitoring was performed using a commercially available ICI system to collect keyhole penetration data in real time. These measurements were reconstructed to generate the vapor capillary shape at different welding parameters. Process parameters significantly influenced the keyhole shape and the keyhole root position relative to the process beam. Finally, the keyhole geometry showed distinct differences between the stainless steel alloys and Ti-6Al-4V.

2205↗
DOE OSTI2025

Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model

Solid-state synthesis is widely used to obtain various inorganic materials, such as battery materials and bulk thermoelectrics. Despite its prevalence, the process remains challenging due to the lack of a general theory and well-understood underlying reaction mechanisms. While prior works have successfully extracted structured datasets from literature, they often neglect product phase purity or yield. In this work, we construct a solid-state synthesis dataset consisting of 80,806 syntheses extracted with a large language model (LLM), including 18,869 reactions with impurity phase(s). Our dataset not only validates expected thermodynamic trends for impurity phase formation but also identifies challenging cases where impurity phases emerge even when the target phase is significantly more stable.

Lee, Sanghoon↗
DOE OSTI2021

Bennett-Brassard 1984 quantum key distribution using conjugate homodyne detection

Optical homodyne detection has been widely used in continuous-variable (CV) quantum information processing for measuring field quadrature. In this paper we explore the possibility of operating a conjugate homodyne detection system in “photon counting” mode to implement discrete-variable (DV) quantum key distribution (QKD). A conjugate homodyne detection system, which consists of a beam splitter followed by two optical homodyne detectors, can simultaneously measure a pair of conjugate quadratures X and P of the incoming quantum state. In classical electrodynamics, X 2 + P 2 is proportional to the energy (the photon number) of the input light. In quantum optics, X and P do not commute and thus the above photon-number measurement is intrinsically noisy. This implies that a blind application of standard security proofs of QKD could result in pessimistic performance. We overcome this obstacle by taking advantage of two special features of the proposed detection scheme. First, the fundamental detection noise associated with vacuum fluctuations cannot be manipulated by an external adversary. Second, the ability to reconstruct the photon number distribution at the receiver's end can place additional constraints on possible attacks from the adversary. As an example, we study the security of the BB84 QKD using conjugate homodyne detection and evaluate its performance through numerical simulations. This study may open the door to a family of QKD protocols, complementary to the well-established DV-QKD based on single-photon detection and CV-QKD based on coherent detection.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
DOE OSTI2021

PV Reference Cells for Outdoor Use: An Investigation of Calibration Factors

Reference cells are widely used in the photovoltaic (PV) industry to measure irradiance. For field applications and outdoor use, a variety of products are on the market, and they are often perceived as a low-cost alternative to thermopile radiometers. But reference cell characteristics differ substantially from the latter, therefore measurements made by one category of instrument cannot be substituted for or directly compared with the other. The National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory is currently in a multiyear effort to develop guidance and recommendations for the design and use of outdoor reference cells, with a view to reducing inconsistencies and measurement uncertainty. The core of this effort is the long-term deployment of 39 units of 10 distinct types made by 6 manufacturers. This report compares the calibration factors provided by manufacturers - which most users rely upon - to those measured at a high level of accuracy by NREL's Cell Lab. It also reports on the application of the Broadband Outdoor Radiometer Calibration (BORCAL) method to reference cells. As a group, the factory calibrations for the crystalline silicon cells showed a small positive bias of +0.7%, with a tight distribution around this value. This is a very good result for the manufacturers. The two World Photovoltaic Scale cells constituted outliers, with a mean deviation of -0.8%, which is still within the reported uncertainty ranges. The data collected during the BORCAL procedures show a strong and systematic fluctuation of the apparent responsivity of the reference cells over the course of a day, which was anticipated. Consequently, the standard BORCAL R@45 calibration factors underestimated the reference cell responsivities by 1%-4%. An alternate responsivity indicator was developed, R'@30, incorporating adjustments for temperature response, spectral response, and directional response. This reduced the mean deviation to -0.4% compared to the Cell Lab. Further, two independent BORCAL sessions were able to produce the same values to within ±0.2%. This could be a viable approach for verifying reference cells using existing BORCAL infrastructure, for example, to evaluate their long-term stability. The daily profiles of responsivity obtained during the BORCAL sessions (provided in Appendix A) show clear patterns of differences between models, which provides evidence that their directional responses differ from each other; thus, two sensors that are calibrated correctly might give the same reading at 30° zenith angle but systematically different readings at 60°. The impact of these inconsistencies in field operation will be quantified using the long-term observations started in 2020.

14 SOLAR ENERGY↗
DOE OSTI2025

Can the Hock Process Be Used to Produce Phenol from Polystyrene?

Polystyrene (PS) is a widely used thermoplastic polymer, but its very low recycling rate has motivated consideration of chemical conversion strategies to convert waste PS into value-added products. Oxidation methods have been widely studied, but they typically generate benzoic acid, a product with a relatively low market demand. Phenol is a higher volume chemical that would be an appealing target, but no methods currently exist for the conversion of PS into phenol. The repeat unit in PS closely resembles cumene, the primary feedstock used to produce phenol through the Hock process. Here, we investigate prospects for adapting the Hock process to PS, generating hydroperoxides through the autoxidation of benzylic C–H bonds followed by the acid-promoted rearrangement of the hydroperoxides to afford phenol and a partially oxygenated polymer. Experimental and computational studies of dimeric and trimeric PS model compounds show that neighboring phenyl rings impose conformational constraints that raise the barrier to hydrogen-atom transfer from the tertiary benzylic C–H bond. These effects are also evident with PS and contribute to lower yields of phenol when PS is subjected to Hock process conditions. Furthermore, these results provide valuable insights that have important implications for other efforts that seek to adapt small-molecule reactivity to polymeric feedstocks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reducing Surface Recombination Velocity of Methylammonium-Free Mixed-Cation Mixed-Halide Perovskites via Surface Passivation

We control surface recombination in the mixed-cation, mixed-halide perovskite, FA0.83Cs0.17Pb(I0.85Br0.15)3, by passivating nonradiative defects with the polymerizable Lewis base (3-aminopropyl)trimethoxysilane (APTMS). We demonstrate average minority carrier lifetimes >4 µs, nearly single exponential monomolecular photoluminescence decays, and high external photoluminescence quantum efficiencies (>20%, corresponding to ~97% of the maximum theoretical quasi-Fermi-level splitting) at low excitation fluence. We confirm both the composition and valence band edge position of the FA0.83Cs0.17Pb(I0.85Br0.15)3 perovskite using multi-institutional, cross-validated, X-ray photoelectron spectroscopy and UV photoelectron spectroscopy measurements. We extend the APTMS surface passivation to higher bandgap double-cation (FA and Cs) compositions (1.7, 1.75, and 1.8 eV) as well as the widely used triple-cation (FA, MA, and Cs) composition. Finally, we demonstrate that the average surface recombination velocity decreases from ~1000 to ~10 cm/s post APTMS passivation for FA0.83Cs0.17Pb(I0.85Br0.15)3. Our results demonstrate that surface-mediated recombination is the primary nonradiative loss pathway in many methylammonium (MA)-free mixed-cation mixed-halide films with a range of different bandgaps, which is a problem observed for a wide range of perovskite active layers and reactive electrical contacts. Our study also provides insights to develop passivating molecules that help reduce surface recombination in MA-free mixed-cation mixed-halide films and indicates that surface passivation and contact engineering will enable near-theoretical device efficiencies with these materials.

14 SOLAR ENERGY↗
DOE OSTI2022

OpenMP Target Task: Tasking and Target Offloading on Heterogeneous Systems

This work evaluated the use of OpenMP tasking with target GPU offloading as a potential solution for programming productivity and performance on heterogeneous systems. Also, it is proposed a new OpenMP specification to make the implementation of heterogeneous codes simpler by using OpenMP target task, which integrates both OpenMP tasking and target GPU offloading in a single OpenMP pragma. As a test case, the authors used one of the most popular and widely used Basic Linear Algebra Subprogram Level-3 routines: triangular solver (TRSM). To benefit from the heterogeneity of the current high-performance computing systems, the authors propose a different parallelization of the algorithm by using a nonuniform decomposition of the problem. This work used target GPU offloading inside OpenMP tasks to address the heterogeneity found in the hardware. This new approach can outperform the state-of-the-art algorithms, which use a uniform decomposition of the data, on both the CPU-only and hybrid CPU-GPU systems, reaching speedups of up to one order of magnitude. The performance that this approach achieves is faster than the IBM ESSL math library on CPU and competitive relative to a highly optimized heterogeneous CUDA version. One node of Oak Ridge National Laboratory’s supercomputer, Summit, was used for performance analysis.

Valero Lara, Pedro↗
DOE OSTI2025

3D mesh regularization within an ALE code using a weighted line sweeping method

The Lagrangian formalism is widely used to simulate hydrodynamic responses in complex engineering applications, particularly those involving strong shock waves. However, as the mesh moves with the fluid, it can become highly distorted, requiring a regularization step. This involves constructing a new grid and remapping conservative quantities onto it to restore mesh quality. This work introduces a regularization method for block-structured meshes within a 3D ALE (Arbitrary Lagrangian-Eulerian) code. The proposed approach prevents mesh tangling while preserving the anisotropic features of the initial Lagrangian mesh. This regularization technique incorporates aspect ratio-based weights to control mesh smoothing. Unlike uniform rezoning techniques, this weighted approach maintains proximity to the Lagrangian mesh while improving mesh quality. Here, the method effectively handles concave geometries by mitigating the grid attraction phenomenon, which typically leads to mesh concentration along concave edges. Numerical experiments demonstrate its efficiency in regularizing severely deformed meshes, and its integration within the ALE framework is validated on challenging hydrodynamic test cases, including the triple point problem.

42 ENGINEERING↗
DOE OSTI2020

Evaluation of Amorphous Oxide Coatings for High-Voltage Li-Ion Battery Applications Using a First-Principles Framework

Cathode surface coatings are widely used industrially as a means to suppress degradation and improve electrochemical performance of lithium-ion batteries. However, developing an optimal coating is challenging, as different coating materials may enhance one aspect of performance while hindering another. Furthermore, to elucidate the fundamental thermodynamic and transport properties of amorphous cathode coating materials, here, we present a framework for calculating and analyzing the Li + and O 2- transport and the stability against delithiation in such materials. Our framework includes systematic workflows of ab-initio molecular dynamics calculations to obtain amorphous structures and diffusion trajectories coupled with an analysis of critical changes of the active-ion local environment during diffusion. Based on these data, we provide an estimate of room-temperature diffusivities, including statistical error bars, and the evaluation of the coating suitability in terms of its ability to facilitate Li + transport while blocking O 2- transport. Finally, we add the thermodynamic stability analysis of the coating chemistry within the operating voltage of common Li-ion cathodes. We apply this framework to two commonly used amorphous coating materials, Al 2 O 3 and ZnO. We find that (1) in general, a higher Li + content increases both Li + and O 2- diffusivities in both Al 2 O 3 and ZnO. Also, Li + and O 2- diffuse much faster in ZnO than in Al 2 O 3 . (2) However, neither Al 2 O 3 nor ZnO is expected to retain a significant concentration of Li + at high charge. (3) ZnO performs much more poorly in terms of O 2- blocking, and hence, Al 2 O 3 is preferred for high-voltage cathode applications. These results will help to quantitatively evaluate amorphous materials, such as metal oxides and fluorides, for different performance metrics and facilitate the development of optimal cathode coatings.

25 ENERGY STORAGE↗
DOE OSTI2024

Identifying Green Solvent Mixtures for Bioproduct Separation Using Bayesian Experimental Design

Liquid–liquid extraction (LLE) is a widely used technique for the separation and purification of liquid-phase products with applications in various industries, including pharmaceuticals, petrochemicals, and renewable chemistry. A critical step in the design of an LLE process is the selection of appropriate solvents. This study presents a new methodology for identifying solvent mixtures for bioproduct separation using Bayesian experimental design (BED). Motivated by the need for environmentally friendly and effective separation methods, we address the challenge of selecting solvent systems that balance separation efficiency, selectivity, and environmental impact while also tackling the difficulty of separating multiple bioproducts using complex solvent systems. Our approach specifically seeks to predict product partition coefficients (log10 Kp values) as thermodynamic parameters underlying solvent selection. The iterative approach integrates Bayesian optimization with experimental measurements to guide solvent selection and leverages COSMO-RS simulations to enhance high-throughput experimentation. Using the design of solvent systems for the separation of lignin-derived aromatic products via centrifugal partition chromatography (CPC) as a case study, we show that within seven iterations/cycles of the methodology, we can identify new mixtures of green solvents that align with CPC design principles. Furthermore, these results demonstrate the efficacy of the BED framework in optimizing green solvent systems for complex separations, highlighting the potential of this method to advance the field of green chemistry and contribute to the development of sustainable industrial processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Preparing angular momentum eigenstates using engineered quantum walks

Coupled angular-momentum eigenstates are widely used in atomic and nuclear physics calculations and are building blocks for spin networks and the Schur transform. To combine two angular momenta J 1 and J 2 , forming eigenstates of their total angular momentum J=J 1 +J 2 , we develop a quantum-walk scheme that does not require inputting O(j 3 ) nonzero Clebsch–Gordan (CG) coefficients classically. In fact, our scheme may be regarded as a unitary method for computing CG coefficients on quantum computers with a typical complexity of O⁡(j) and a worst-case complexity of O⁡(j 3 ). Equivalently, our scheme provides decompositions of the dense CG unitary into sparser unitary operations. Our scheme prepares angular-momentum eigenstates using a sequence of Hamiltonians to move an initial state deterministically to desired final states, which are usually highly entangled states in the computational basis. In contrast with usual quantum walks, whose Hamiltonians are prescribed, we engineer the Hamiltonians in su⁡(2)×su⁡(2), which are inspired by, but different from, Hamiltonians that govern magnetic resonances and dipole interactions. To achieve a deterministic preparation of both ket and bra states, we use projection and destructive interference to double pinch the quantum walks, such that each step is a unit-probability population transfer within a two-level system. We test our state preparation scheme on classical computers, reproducing tables of CG coefficients. Finally, we also implement small test problems on current quantum hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Identifying Genomic Islands with Deep Neural Networks

Background Horizontal gene transfer is the main source of adaptability for bacteria, through which genes are obtained from different sources including bacteria, archaea, viruses, and eukaryotes. This process promotes the rapid spread of genetic information across lineages, typically in the form of clusters of genes referred to as genomic islands (GIs). Different types of GIs exist, and are often classified by the content of their cargo genes or their means of integration and mobility. While various computational methods have been devised to detect different types of GIs, no single method is capable of detecting all types. Results We propose a method, which we call Shutter Island, that uses a deep learning model (Inception V3, widely used in computer vision) to detect genomic islands. The intrinsic value of deep learning methods lies in their ability to generalize. Via a technique called transfer learning, the model is pre-trained on a large generic dataset and then re-trained on images that we generate to represent genomic fragments. We demonstrate that this image-based approach generalizes better than the existing tools. Conclusions We used a deep neural network and an image-based approach to detect the most out of the correct GI predictions made by other tools, in addition to making novel GI predictions. The fact that the deep neural network was re-trained on only a limited number of GI datasets and then successfully generalized indicates that this approach could be applied to other problems in the field where data is still lacking or hard to curate.

Computer Vision↗
DOE OSTI2020

miniGAN: A Generative Adversarial Network proxy application WBS 2.2.6.08 ECP-2.1.3 (Q3 FY2020 Milestone Report) (V.1.0)

In order to support the machine learning co-design needs of ECP applications in current and future DOE HPC hardware, we have developed a generative adversarial network (GAN) proxy application, miniGAN, that has been released through the ECP proxy application suite. The proxy application is representative of the needs of ExaLearn's target applications, specifically the Cosmoflow and ExaGAN cosmology applications and the ExaWind energy application. The proxy application also demonstrates the first use of performance portable kernels within widely-used machine learning frameworks: PyTorch (Facebook) and Horovod (Uber). We provide performance scaling results for similar workloads to ExaGAN and a profile of individual GAN training components.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Hand Calculation Methods for Nuclear Criticality Safety

This primer provides an overview of the most common hand calculation methods used for criticality safety calculations. The most widely used tools available to a nuclear criticality safety (NCS) practitioner are probably the common Monte Carlo or deterministic criticality safety codes, which can be used to model very complex systems. However, use of these codes can obscure the parameters to which a particular fissile system may be sensitive, whereas the hand calculation methods can be used to delve into the ways each parameter may affect the reactivity of a fissile material system. Furthermore, practitioners must avoid using computer codes as devices that take inputs and simply provide outputs (i.e., a “black box”). Many years ago, pioneers such as Joe Thomas, David Smith, and Hugh Paxton, among others in the field of nuclear criticality safety, took the time before the advent of high-speed desktop computers to create simple hand methods for criticality safety analyses. Some of the methods can be used for single fissile units; others are applicable to fissile units arranged into simple array configurations. This primer discusses the applicability of the various methods, illustrates how they are used, and provides an interpretation of the various results. The NCS practitioner will need to spend time to master the methods that could be most useful; however, they can provide the practitioner with fast and accurate answers to criticality safety problems if they are used correctly and if critical data exist for the problem at hand. Hand calculation methods can be used as a starting point for more advanced calculations, and in many circumstances, they can provide sensitivity and perturbation information more quickly than using a criticality code.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

A Novel Machine Learning Algorithm for Cloud Detection Using AERI Measurement Data

Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Disrupting EV Charging Sessions and Gaining Remote Code Execution with DoS, MITM, and Code Injection Exploits using OCPP 1.6

Open Charge Point Protocol (OCPP) 1.6 is widely used in the electric vehicle (EV) charging industry to communicate between Charging System Management Services (CSMSs) and Electric Vehicle Supply Equipment (EVSE). Unlike OCPP 2.0.1, OCPP 1.6 uses unencrypted websocket communications to exchange information between EVSE devices and an onpremise or cloud-based CSMS. In this work, we demonstrate two machine-in-the-middle (MITM) attacks on OCPP sessions to terminate charging sessions and gain root access to the EVSE equipment via remote code execution (RCE). Second, we demonstrate a malicious firmware update with a code injection payload to compromise an EVSE. Lastly, we demonstrate two methods to prevent availability of the EVSE or CSMS. One of these, originally reported by SaiFlow, prevents traffic to legitimate EVSE equipment using a DoS-like attack on CSMSs by repeatedly connecting and authenticating several CPs with the same identities as the legitimate CP. These vulnerabilities were demonstrated with proof-of-concept exploits in a virtualized Cyber Range at Wright State University and/or with a 350 kW Direct Current Fast Charger (DCFC) at Idaho National Laboratory. The team found that OCPP 1.6 could be protected from these attacks by adding secure shell (SSH) tunnels to the protocol, if upgrading to OCPP 2.0.1 was not an option. Index Terms—Electric vehicle charging, cybersecurity, OCPP, cyberattack, cyber-resilience. INSPEC Accession Number: 23981565

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Neural network ensembles and uncertainty estimation for predictions of inelastic mechanical deformation using a finite element method-neural network approach

The finite element method (FEM) is widely used to simulate a variety of physics phenomena. Approaches that integrate FEM with neural networks (NNs) are typically leveraged as an alternative to conducting expensive FEM simulations in order to reduce the computational cost without significantly sacrificing accuracy. However, these methods can produce biased predictions that deviate from those obtained with FEM, since these hybrid FEM-NN approaches rely on approximations trained using physically relevant quantities. In this work, an uncertainty estimation framework is introduced that leverages ensembles of Bayesian neural networks to produce diverse sets of predictions using a hybrid FEM-NN approach that approximates internal forces on a deforming solid body. The uncertainty estimator developed herein reliably infers upper bounds of bias/variance in the predictions for a wide range of interpolation and extrapolation cases using a three-element FEM-NN model of a bar undergoing plastic deformation. This proposed framework offers a powerful tool for assessing the reliability of physics-based surrogate models by establishing uncertainty estimates for predictions spanning a wide range of possible load cases.

42 ENGINEERING↗