Stringent Conditions Research Articles

High-strength elastomer separator for high-current-density-charging lithium metal batteries

Utilizing lithium metal as an anode in batteries has been expected to replace conventional lithium-ion batteries. However, the mechanical properties and electrochemical performance of current separators do not meet the requirements for practical applications of lithium metal batteries (LMBs). Here, we report an elastomer separator with an interconnected structure of plastic-crystal-embedded and garnet-conductor-regulated thermoplastic polystyrene-b-poly(ethylene-r-butylene)-b-polystyrene elastomer integrated with the polyethylene matrix. The 14-micron-thick elastomer separators show a combination of excellent elongation of ∼115.2% and sufficiently high tensile strength of ∼56 MPa. The elastomer separators accommodate volume changes and block dendrites for high-current-density cycling of LMBs. As a demonstration, the elastomer separators enable stable operation of LMBs under stringent conditions, a practical high loading of 18 mg cm−2 LiNi0.8Co0.1Mn0.1O2 (NCM811) cathode at an extremely high charging/discharging current density of 1.8 mA cm−2, delivering a high reversible capacity of 164 mAh g−1 and capacity retention of 88% after 140 cycles.

Fluorescence tunable carbon dots for in vitro nuclear dynamics and gastrointestinal imaging in live zebrafish and their in vivo toxicity evaluation by cardio-craniofacial disfunction assessment.

Sub-cellular organelle anomalies are frequently observed in diseases such as cancer. Early and precise diagnosis of these alterations can be crucial for patient outcomes. However, current diagnostic tools using conventional organic dyes or metal quantum dots face limitations, including poor biocompatibility, stringent storage conditions, limited solubility in aqueous media, and slow staining speeds. These challenges underscore the need for safer, more effective diagnostic and therapeutic solutions. In these aspects, we have developed highly photostable, biocompatible, water-dispersible carbon dots (TNCDs) with an average size of 5.5 nm using tartaric acid and ethylenediamine via a hydrothermal route. The synthesized TNCDs have shown bright blue fluorescence under the irradiation of UV-light at an excitation wavelength of 365 nm. They exhibit a quantum yield (QY) of 25.1% with maximum emission at 390 nm. A nice tri-exponential fitting of the decay curve with characteristic lifetimes of 1.52 ns, 3.05 ns and 6.11 ns for TNCDs was obtained. In vitro studies demonstrated that TNCDs have high biocompatibility (20 μg ml-1) with almost 100% cell viability and excellent nucleus targeting and staining capabilities with low background interference (with 10-12 times enhancement in fluorescence intensity). Additionally, if tagged with photosensitizers or radionuclides, TNCDs can serve as therapeutic agents in photodynamic therapy against cancer cells. Importantly, TNCDs exhibited negligible toxicity in developing zebrafish even at high concentrations (up to 400 mg L-1) as investigated by cardio and craniofacial disfunction assessment. Live organism imaging revealed that TNCDs produced aggregation-induced strong and specific green fluorescence in the gut of zebrafish larvae even at low concentrations, indicating their potential for nucleus staining and gut-specific optical imaging (at 50 mg L-1). Thus, our TNCDs represent a robust nanoplatform for cellular and whole-organism fluorescence imaging, offering both diagnostic and therapeutic potential.

Therapeutic effects of intracerebral transplantation of human modified bone marrow-derived stromal cells (SB623) with voluntary and forced exercise in a rat model of ischemic stroke.

Ischemic stroke results in significant long-term disability and mortality worldwide. Although existing therapies, such as recombinant tissue plasminogen activator and mechanical thrombectomy, have shown promise, their application is limited by stringent conditions. Mesenchymal stem cell (MSC) transplantation, especially using SB623 cells (modified human bone marrow-derived MSCs), has emerged as a promising alternative, promoting neurogenesis and recovery. This study evaluated the effects of voluntary and forced exercise, alone and in combination with SB623 cell transplantation, on neurological and psychological outcomes in a rat model of ischemic stroke. Male Wistar rats that had undergone middle cerebral artery occlusion (MCAO) were divided into six groups: control, voluntary exercise (V-Ex), forced exercise (F-Ex), SB623 transplantation, SB623 + V-Ex, and SB623 + F-Ex. Voluntary exercise was facilitated using running wheels, while forced exercise was conducted on treadmills. Neurological recovery was assessed using the modified neurological severity score (mNSS). Psychological symptoms were evaluated through the open field test (OFT) and forced swim test (FST), and neurogenesis was assessed via BrdU labeling. Both exercise groups exhibited significant changes in body weight post-MCAO. Both exercises enhanced the treatment effect of SB623 transplantation. The forced exercise showed a stronger treatment effect on ischemic stroke than voluntary exercise alone, and the sole voluntary exercise improved depression-like behavior. The SB623 + F-Ex group demonstrated the greatest improvements in motor function, infarct area reduction, and neurogenesis. The SB623 + V-Ex group was most effective in alleviating depression-like behavior. Future research should optimize these exercise protocols and elucidate the underlying mechanisms to develop tailored rehabilitation strategies for stroke patients.

Fractal-domain Transformer Based on Learnable Multifractal Spectrum for Chaotic Systems Classification

Conventional deep learning in the spatiotemporal-frequency domain frequently encounter challenges in terms of slow convergence rates and limited generalization, particularly for classification of chaotic systems. To address these limitations, this paper introduces a novel fractal-inspired deep network model, specifically, the Multifractal Spectrum Transformer (MFS-Transformer), grounded in learnable multifractal analysis. Initially, we put forward the conceptual framework of fractal learning, compared with traditional fractal signal processing methodologies and spatial-temporal domain learning paradigms. Subsequently, a Learnable Multifractal Spectrum (LMFS) derived from 3D spatial gridding, coupled with fractal-domain filtering, is proposed to construct the iterative learning process within the fractal domain. Further, we formulate the MFS-Transformer, an innovative architecture that integrates multi-channel embedding, LMFS, fractal-domain filtering, residual fusion mechanisms, a mixer module, and a classifier, tailored for chaotic system classification. Ultimately, we evaluate the efficacy of our model in classifying 3D chaotic systems under stringent conditions of short-term sequences and low Signal-to-Noise Ratio (SNR). Experimental outcomes underscore the substantial performance gains achieved by the MFS-Transformer, with classification accuracy enhancements of 13.34% and 5.00% over existing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), respectively, under SNR=0dB and 32-sample sequences. These findings validate the superiority of the MFS-Transformer in addressing the complexities of chaotic system classification under complex scenarios. This research not only advances the frontier of fractal deep learning but also presents a novel perspective and methodology for tackling intricate spatiotemporal classification problems.

A new robust indirect adaptive control for torque-driven robot manipulators under relaxed excitation conditions

This paper presents a new robust adaptive joint position trajectory tracking control for torque-driven robot manipulators using a novel parameter identification scheme. The novelty of this identification scheme is that ensures convergence of the true parameters exponentially, even when the regressor do not verify the stringent condition of persistent excitation, using a classical normalized gradient descendent adaptive law. An indirect adaptive controller approach is used to combine the identification scheme with a robust model based GES controller to verify joint position trajectory tracking control objectives under constant disturbances. The validity of our claims are illustrated with simulations of a two degrees-offreedom robot manipulator.

OAM-basis underwater single-pixel imaging based on deep learning at a low sampling rate

Our study introduces a pioneering underwater single-pixel imaging approach that employs an orbital angular momentum (OAM) basis as a sampling scheme and a dual-attention residual U-Net generative adversarial network (DARU-GAN) as reconstruction algorithm. This method is designed to address the challenges of low sampling rates and high turbidity typically encountered in underwater environments. The integration of the OAM-basis sampling scheme and the improved reconstruction network not only enhances reconstruction quality but also ensures robust generalization capabilities, effectively restoring underwater target images even under the stringent conditions of a 3.125% sampling rate and 128 NTU turbidity. The integration of OAM beams’ inherent turbulence resistance with DARU-GAN's advanced image reconstruction capabilities makes it an ideal solution for high-turbid underwater imaging applications.

A 0.73 dB Multi-Gain Low Noise Amplifier Design with Fast Mode-Switching for 5G/4G Applications.

In this paper, a sub-1dB Low Noise Amplifier (LNA) with several gain modes, including amplification and attenuation modes required for the fifth and fourth generations (5G/4G) of mobile network applications, is proposed. Its current consumption is adaptive for every gain mode and varies to lower currents for lower amplifications due to the importance of current consumption for mobile network applications. The proposed LNA features an innovative architecture with a three-core input structure supporting multi-gain modes, achieving high gain and ultra-low noise performance. Additionally, the design integrates a cascade switching mechanism to ensure fast transitions between the gain modes and maintain operational stability. A reconfigurable input structure is introduced to support multiple input stages, enabling the proposed LNA to be compatible with both 5G and 4G applications. The proposed design demonstrates the implementation of seven distinct gain modes with a maximum current consumption of 11.68 mA, achieving proper input matching in each gain mode. The LNA delivers a maximum gain of 20.4 dB with a noise figure of 0.73 dB. Moreover, the most stringent mode switching condition achieved, the ON time, is as short as 1.295 µs, and the gain mode transition speed is an impressive 0.874 µs, ensuring extremely fast mode transitions. The proposed LNA occupies an area of 700 µm × 500 µm and is fabricated using a 65 nm FD-SOI process.

Bioleaching of Metal-Polluted Mine Tailings: A Comparative Approach Between Ex Situ Slurry-Phase Stirred Reactors Versus In Situ Electrokinetic Percolation

This work compares two technologies for the remediation of metal-polluted mine tailings based on lab-scale bioleaching experiments performed in (a) conventional agitated slurry-phase reactors and (b) in situ electrokinetic percolation. While ex situ bioleaching in agitated reactors has been widely studied, only a few previous works have studied the in situ option that couples bioleaching and electrokinetics. Real mine tailings from an abandoned sphalerite mine in southern Spain were used. The leaching medium was externally generated in a bioreactor using an autochthonous acidophilic culture and then added to tailings in batch experiments. This medium enabled metal leaching from mine tailings without the stringent operating conditions required by a classic bioleaching process. Metal removal efficiencies and kinetic rate constants after 15 d of treatments were calculated. Additionally, advantages or disadvantages between the two methods were discussed. The results for the innovative EK-percolation method showed rates and efficiencies that were comparable to, and in some cases better than, those achieved with conventional stirred slurry systems.

Stability Analysis of Split‐Step θ$$ \theta $$‐Milstein Scheme for Stochastic Delay Integro‐Differential Equations

ABSTRACTThis paper introduces and analyzes a split‐step ‐Milstein (SSTM) scheme specifically designed for stochastic delay integro‐differential equations (SDIDEs). Given reasonable assumptions regarding the drift term, diffusion term, and integral term, it is established that for small stepsize constraint, the SSTM method with is mean square exponential stable. Additionally, the result shows that also demonstrates the same stability under more stringent conditions on stepsize. Ultimately, two numerical experiments are presented to verify the validity of the obtained results.

Assessing Cytotoxicity, Proteolytic Stability, and Selectivity of Antimicrobial Peptides: Implications for Orthopedic Applications.

In orthopedics, the use of anti-infective biomaterials is considered the most promising strategy to contrast the bacterial contamination of implant surfaces and reduce the infection rate. KSL, KSL-W, and Dadapin-1 are three antimicrobial peptides (AMPs) that possess significant antibacterial properties, making them promising candidates for producing anti-infective biomaterials not based on antibiotics. To fully assess their true potential, this study explores in detail their cytocompatibility on human osteoblast-like MG63 cells, murine fibroblastoid L929 cells, and hMSCs. To this end, the cytotoxicity of the AMPs in terms of IC50 was tested over a range of concentrations of 450-0.22 µg/mL using the ATP bioluminescence assay. The tests were performed both in the presence and absence of bovine serum to assess the effects of serum components on peptide stability. IC50 values obtained under the most stringent conditions were used to extrapolate the selectivity index (S.I.) toward salient bacterial species. In medium containing serum, all AMPs exhibited minimal to no cytotoxicity, with IC50 values exceeding 100 µg/mL. Dadapin-1 was the peptide that exhibited the lowest cytotoxicity, KSL-W exhibited the highest stability, and KSL exhibited the highest selectivity. Overall, these findings highlight the potential of these AMPs for the future production of anti-infective materials.

Quantitative Characterization of Partitioning Stringency in SELEX.

Maintaining stringent conditions in SELEX (Systematic Evolution of Ligands by EXponential enrichment) is crucial for obtaining high-affinity aptamers. However, excessive stringency greatly increases the risk of SELEX failure. Controlling stringency has remained a technical challenge, largely dependent on intuition, due to the absence of a clear, quantitative measure of stringency. This study was motivated by our insight that, while stringency is influenced by multiple factors, it can be quantified by its effect: increasing stringency reduces the quantity of binders normalized to that of nonbinders after partitioning. Based on this insight, we propose measuring stringency using the binder-to-nonbinder ratio (BNR), where a lower BNR indicates higher stringency. We derive an experimental method for determining BNR via quantitative PCR. Our theoretical analysis and SELEX experiments using two distinct proteins as selection targets underscore the importance of maintaining a BNR significantly greater than zero to avoid failure, a principle we call the SELEX nonfailure criterion. By employing inverse BNR to quantify stringency and applying this criterion, researchers can more rationally control SELEX progress. The quantitative stringency measure and nonfailure criterion can also be applied to other artificial evolution methods, provided that selected binders are quantifiable.

Attacking medical images with minimal noise: exploiting vulnerabilities in medical deep-learning systems.

A change in the output of deep neural networks (DNNs) via the perturbation of a few pixels of an image is referred to as an adversarial attack, and these perturbed images are known as adversarial samples. This study examined strategies for compromising the integrity of DNNs under stringent conditions, specifically by inducing the misclassification of medical images of disease with minimal pixel modifications. This study used the following three publicly available datasets: the chest radiograph of emphysema (cxr) dataset, the melanocytic lesion (derm) dataset, and the Kaggle diabetic retinopathy (dr) dataset. To attack the medical images, we proposed a method termed decrease group differential evolution (DGDE) for generating adversarial images. Under this method, a noise matrix of the same size as the input image is first used to attack the image sample several times until the initial adversarial perturbation s0 is obtained. Next, a subset s1 is randomly picked from the initial adversarial perturbation s0 that is still able to cause the samples to be misclassified by the classifier. A new subset s2 is subsequently randomly selected from the adversarial perturbation subset s1, which can still cause the adversarial samples to be misclassified by the classifier. Finally, the adversarial perturbation subset sn with the minimum number of elements is obtained by continuous reduction of the number of perturbed pixels. In this study, the DGDE method was used to attack the images of the cxr dataset, the derm dataset, and the dr dataset; and the minimum number of pixels required to be considered a successful attack was 11, 7, and 7, respectively, while the maximum number of pixel changes was 55, 35, and 21, respectively. The average number of pixel changes was 30, 18, and 11, respectively, in the cxr dataset, the derm dataset, and the dr dataset, respectively, while the percentages of the average number of pixel changes among the total number of pixels of the image were 0.0598%, 0.0359%, and 0.0219, respectively. Unlike the traditional differential evolution (DE) method, the proposed DGDE method modifies fewer pixels to generate adversarial samples by introducing a variable population number and a novel crossover and selection strategy. However, the success rate of the initial attack on different image datasets varied greatly. In future studies, we intend to identify the reasons for this phenomenon and improve the success rate of the initial attack.

Comparative mineralogical characterization of REEs ore samples from the Mushgai Khudag and Lugiin Gol deposits, Mongolia

Mongolia, situated in the Central Asian orogenic belt, is characterized by geology that is highly conducive to the abundance of mineral resources, including significant rare earth elements (REEs) reserves. However, limited knowledge of the mineralogy of its deposits has hindered the development of efficient REEs extraction methods. This study presents a detailed investigation of the mineralogical characteristics of Mongolian ores and analyzes their compositions, mineral associations, liberation, and REEs concentrations via advanced analytical techniques, including the TESCAN Integrated Mineral Analyzer (TIMA). Additionally, the BCR sequential extraction method was employed to determine the binding and leaching properties of REEs. Our results revealed that the two ore samples were predominantly rich in light rare earth elements (LREEs), with REE-bearing minerals closely associated with gangue minerals such as calcite, albite, and quartz, leading to poor liberation. The extraction data indicated that >85 % of the REEs in the Mushgai Khudag deposit were retained in the residual fraction, whereas in the Lugiin Gol deposit, the majority of the REEs were associated with the reducible fraction. These findings underscore the challenges associated with extracting REEs from Mongolian deposits, which demand stringent conditions for effective recovery. This research contributes valuable insights into the complex mineralogy of Mongolian REEs deposits, offering a foundation for future advancements in REE extraction technologies.

3D micro-lattice structural hydrogel evaporator with super salt resistance for solar desalination of high-salinity

The production of freshwater from seawater via solar energy is currently a hot research topic. Nonetheless, the accumulation of salt on the evaporator’s surface during the evaporation process significantly impedes both the rate and efficiency of water evaporation. Consequently, there is an urgent necessity to ingeniously design the evaporator structure in order to mitigate salt precipitation, marking a critical challenge in device engineering. This study presents a pioneering 3D micro-lattice structural hydrogel evaporator, meticulously crafted utilizing state-of-the-art 3D printing technology, tailored expressly for the solar desalination of highly saline waters. Its hierarchical porous architecture amplifies water transportation and light absorption capabilities, concurrently enabling swift relocation of salt ions away from the evaporative surface, thereby thwarting salt buildup. Remarkably, the hydrogel preserves its structural robustness amidst high-concentration brine environments and fosters proficient heat concentration at the air–water interface, yielding freshwater output rates surpassing 3.5 kg m−2 h−1 even under stringent conditions of 20 wt% NaCl solutions, exhibiting unparalleled performance and durability across multiple operational cycles. This research paves the way for sustainable and efficient solar desalination systems, showcasing the potential of micro-lattice structures in hydrogels for enhanced salt rejection and improved water recovery rates in challenging environments.

Analysis of a Single Cell RNA-seq Workflow by Random Matrix Theory Methods.

Single cell RNA-seq (scRNAseq) workflows typically start with a count matrix and end with the clustering of sampled cells. While a range of methods have been developed to cluster scRNAseq datasets, no theoretical tools exist to explain why a particular cluster exists or why a hypothesized cluster is missing. Recently, several authors have shown that eigenvalues of scRNAseq count matrices can be approximated using random matrix models. In this work, we extend these previous works to the study of a scRNAseq workflow. We model scaled count matrices using random matrices with normally distributed entries. Using these random matrix models, we quantify the differential expression of a cluster and develop predictions for the workflow, and in particular clustering, as a function of the differential expression. We also use results from random matrix theory (RMT) to develop predictive formulas for portions of the scRNAseq workflow. Using simulated and real datasets, we show that our predictions are accurate if certain conditions hold on differential expression, with our RMT based predictions requiring particularly stringent condition. We find that real datasets violate these conditions, leading to bias in our predictions, but our predictions are better than a naive estimator and we point out future work that can improve the predictions. To our knowledge, our formulas represents the first predictive results for scRNAseq workflows.

Enhanced Durability and Performance of SOEC Stacks at Intermediate-Low Temperature Operation

Recognizing the urgent need for sustainable and efficient energy technologies, we have investigated the durability of Solid Oxide Electrolysis Cells (SOECs) over a demanding 1000-hour period at a constant temperature of 650°C. A key discovery was that SOECs equipped with advanced conductive coatings on the separators exhibited a significantly lower degradation rate(-0.7%) compared to traditional cells(29.0%). Cells using these coated separators maintained structural integrity and demonstrated excellent performance at low voltages.Testing under stringent conditions highlighted the exceptional durability of the coated separators, which is essential for large-scale hydrogen production. The low degradation rate and consistent voltage demonstrate the revolutionary potential to enhance the stability and long-term efficiency of SOECs operated at 650°C.This research proves the critical importance of separator coatings in developing SOECs that perform operate continuously and perform excellently at low voltage levels in intermediate-low temperature conditions. This study is significant as it presents the first instance of a 650°C operating SOEC stack capable of stable, long-term operation over 1000 hours. Such advancements support the introduction of SOECs into the clean energy market and set the stage for the next generation of technologies essential for a sustainable energy future.

Enzymatic Cleavage of Double-Stranded DNA-Encoded Libraries (DELs) to Single-Stranded DELs with Compounds at the 3' End: Its Application in Photo-Crosslinking Selection.

DNA-encoded library (DEL) technology is a crucial tool in pharmaceutical research, rapidly identifying compounds that bind to a target of interest from an extensive pool of compounds. In this study, we propose a new method for generating single-stranded DELs (ssDELs) with compounds at the 3' end. The introduction of uniquely designed hairpin-shaped headpieces containing deoxyuridine (NC-HP) and the use of a cleavage enzyme facilitate the conversion from double-stranded DELs (dsDELs) to such ssDELs. Moreover, Klenow fill-in provides the dsDELs with photo-crosslinkers covalently linked to the coding region, which exhibit durability even under stringent washing conditions and enable photo-crosslinking with a high signal-to-noise ratio, as also confirmed in cell-based photo-crosslinking selections.

Robust Adaptive Extremum Seeking Control Without Persistence of Excitation: Theory to Experiment

ABSTRACTIn this article, we develop a novel adaptive extremum‐seeking control (AdESC) algorithm with robustness guarantees and without persistence of excitation (PE). Specifically, this builds on a proportional‐integral (PI)‐like parameter estimator. A zeroth‐order optimization framework is used, where the optimizer/agent can only query the numerical value of the cost function at the current coordinate given an unmodeled bounded disturbance. Since parameter estimation plays a decisive role in the stability and convergence properties of AdESC algorithm, it is also well established in the existing literature that to ensure parameter convergence a stringent PE condition is required. Here, we eliminate the need for a stringent PE condition by utilizing a novel set of weighted integral filter dynamics, while ensuring sufficient richness using a milder condition, called initial excitation (IE). Moreover, to validate the robustness guarantees towards unmodeled bounded disturbance, a detailed Lyapunov function based analysis is performed to establish the closed‐loop stability and convergence in the form of uniform ultimate boundedness (UUB). Furthermore, an experimental study using a unicycle wheeled mobile robot (WMR) is carried out as a proof‐of‐concept considering disturbance and disturbance‐free scenarios.

TRUST-DART: Land Surfaces Temperature and Emissivity Nonlinear Mapping from Nonisothermal Mixed Pixels of Satellite Images with the DART 3D Radiative Transfer Model

Abstract. Coarse spatial resolution of Thermal Infrared (TIR) satellites hampers measuring the temperature and emissivity of scene elements from space that improves our understanding of land surfaces' thermal behavior. The stringent conditions of current TIR unmixing methods hinder the production of extensive component temperature and emissivity products. To address this, we designed a gradient-based multi-pixel physical model, TRUST-DART, to derive the temperature and emissivity of urban features from non-isothermal mixed pixels of satellite images using the DART 3D radiative transfer model. Unlike traditional TIR unmixing methods, TRUSTDART is not constrained by issues related to spatial, spectral, temporal resolution, angular, scene, field measurement requirements, or manual operations. Its inputs include an at-surface radiance image, downwelling sky irradiance, a 3D urban mock-up with feature information, and DART input parameters such as spatial resolution. It generates maps of emissivity and temperature per urban feature. Its accuracy is validated for two vegetation and urban scenes and two types of images (DART simulated pseudo satellite and ASTER observed images). The accuracy of the TRUST-DART depends heavily on the fraction of components. TRUST-DART proves robust for high-fraction components. However, its accuracy decreases with decreasing fractions. TRUST-DART is distributed with DART and is available for education and research via Toulouse III University (https://dart.omp.eu).

The Arizona Molecular ISM Survey with the SMT: Survey Overview and Public Data Release

The CO(1–0) line has been carefully calibrated as a tracer of molecular gas mass. However, recent studies often favor higher J transitions of the CO molecule, which are brighter and accessible for redshift ranges where CO(1–0) is not. These lines are not perfect analogs for CO(1–0), owing to their more stringent excitation conditions, and must be calibrated for use as molecular gas tracers. Here, we introduce the Arizona Molecular ISM Survey with the SMT, a multi-CO line survey of z ∼ 0 galaxies conducted to calibrate the CO(2–1) and CO(3–2) lines. The final survey includes CO(2–1) spectra of 176 galaxies and CO(3–2) spectra for a subset of 45. We supplement these with archival CO(1–0) spectra from xCOLD GASS for all sources and additional CO(1–0) observations with the Kitt Peak 12 m Telescope. Targets were selected to be representative of the 109 M ⊙ ≤ M * ≤ 1011.5 M ⊙ galaxy population. Our project emphasized careful characterization of statistical and systematic uncertainties to enable studies of trends in CO line ratios. We show that optical and CO disk sizes are on average equal, for both the CO(1–0) and CO(2–1) line. We measure the distribution of CO line luminosity ratios, finding medians (16th–84th percentile) of 0.71 (0.51–0.96) for the CO(2–1)-to-CO(1–0) ratio, 0.39 (0.24–0.53) for the CO(3–2)-to-CO(1–0) ratio, and 0.53 (0.41–0.74) for the CO(3–2)-to-CO(2–1) ratio. A companion paper presents our study of CO(2–1)'s applicability as a molecular gas mass tracer and search for trends in the CO(2–1)-to-CO(1–0) ratio. Our catalog of CO line luminosities is publicly available.