Hasil untuk "Chemistry"

M. Bandini, A. Eichholzer

J. Nie, Hong-Chao Guo, D. Cahard et al.

Jean‐François Lutz

E. Pelizzetti, N. Serpone

W. Pryor

Yu Bai, I. Mora‐Seró, F. De Angelis et al.

Yu Bai,†,‡ Ivań Mora-Sero,́ Filippo De Angelis, Juan Bisquert, and Peng Wang*,† †State Key Laboratory of Polymer Physics and Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China ‡Institute of Chemistry and Energy Material Innovation, Academy of Fundamental Interdisciplinary Sciences, Harbin Institute of Technology, Harbin 150080, China Photovoltaic and Optoelectronic Devices Group, Departament de Física, Universitat Jaume I, 12071 Castello,́ Spain Istituto CNR di Scienze e Tecnologie Molecolari, c/o Dipartimento di Chimica, Universita ̀ di Perugia, via Elce di Sotto 8, I-06123 Perugia, Italy

Xin Zhou, Songyi Lee, Zhaochao Xu et al.

Eric Nestor Tseng, Jonas Björk, Risha Achaiah Iythichanda et al.

Low dimensional materials are critical for enabling next generation applications that are central to addressing critical global challenges. Titanium dioxide nanostructures stand out due to their structural versatility and relevance to catalysis, energy conversion, and environmental remediation. Here, we employ a combination of advanced electron microscopy, spectroscopy, and first principles theoretical calculations to investigate the structural and chemical properties of one and two dimensional lepidocrocite type titania. Special emphasis is placed on the one dimensional material, which exhibits anisotropic growth, extending exclusively along a single crystallographic direction. Our analysis suggests that this unusual growth behavior can be attributed to light element impurities, such as carbon, that are incorporated during the bottom up synthesis. The results extend the understanding for these unexplored low dimensional titania materials and offer fundamental insights into their structure and chemistry.

Nicholas Bauman, Ajay Panyala, Libor Veis et al.

The effective deployment and application of advanced methodologies for quantum chemistry is inherently linked to the optimal usage of emerging and highly diversified computational resources. This paper examines the synergistic utilization of Micron memory technologies and Azure Quantum Element cloud computing in Density Matrix Renormalization Group (DMRG) simulations leveraging coupled-cluster (CC) downfolded/effective Hamiltonians based on the double unitary coupled cluster (DUCC) Ansatz. We analyze the performance of the DMRG-DUCC workflow, emphasizing the proper choice of hardware that reflects the numerical overheads associated with specific components of the workflow. We report a hybrid approach that takes advantage of Micron CXL hardware for the memory capacity intensive CC downfolding phase while employing AQE cloud computing for the less resource-intensive DMRG simulations. Furthermore, we analyze the performance of the scalable ExaChem suite of electronic simulations conducted on Micron prototype systems.

Lynn Maria Schneider, Benedikt Sochor, Marcus Johansen et al.

G. Perotti, L. Cacciapuoti, N. -D. Tung et al.

Over the past decade, progress in observational capabilities, combined with theoretical advancements, have transformed our comprehension of the physics and chemistry during planet formation. Despite these important steps forward, open questions persist on the chemical and physical evolution of solids in their journey from the collapsing molecular cores to disks and planetary bodies. This chapter is a repository of such burning questions. It has the ambition to identify the most promising avenues for future research based on current observational and modeling opportunities.

Lin Guo, Xiaokai Yang, Zhonghua Zheng et al.

Uncertainty quantification during atmospheric chemistry modeling is computationally expensive as it typically requires a large number of simulations using complex models. As large-scale modeling is typically performed with simplified chemical mechanisms for computational tractability, we describe a probabilistic surrogate modeling method using principal components analysis (PCA) and Ensemble Sparse Identification of Nonlinear Dynamics (E-SINDy) to both automatically simplify a gas-phase chemistry mechanism and to quantify the uncertainty introduced when doing so. We demonstrate the application of this method on a small photochemical box model for ozone formation. With 100 ensemble members, the calibration $R$-squared value is 0.96 among the three latent species on average and 0.98 for ozone, demonstrating that predicted model uncertainty aligns well with actual model error. In addition to uncertainty quantification, this probabilistic method also improves accuracy as compared to an equivalent deterministic version, by $\sim$60% for the ensemble prediction mean or $\sim$50% for deterministic prediction by the best-performing single ensemble member. Overall, the ozone testing root mean square error (RMSE) is 15.1% of its root mean square (RMS) concentration. Although our probabilistic ensemble simulation ends up being slower than the reference model it emulates, we expect that use of a more complex reference model in future work will result in additional opportunities for acceleration. Versions of this approach applied to full-scale chemical mechanisms may result in improved uncertainty quantification in models of atmospheric composition, leading to enhanced atmospheric understanding and improved support for air quality control and regulation.

Adam D. Rains, Thomas Nordlander, Stephanie Monty et al.

Detailed chemical studies of F/G/K -- or Solar-type -- stars have long been routine in stellar astrophysics, enabling studies in both Galactic chemodynamics, and exoplanet demographics. However, similar understanding of the chemistry of M and late-K dwarfs -- the most common stars in the Galaxy -- has been greatly hampered both observationally and theoretically by the complex molecular chemistry of their atmospheres. Here we present a new implementation of the data-driven \textit{Cannon} model, modelling $T_{\rm eff}$, $\log g$, [Fe/H], and [Ti/Fe] trained on low-medium resolution optical spectra ($4\,000-7\,000\,$\SI{}{\angstrom}) from 103 cool dwarf benchmarks. Alongside this, we also investigate the sensitivity of optical wavelengths to various atomic and molecular species using both data-driven and theoretical means via a custom grid of MARCS synthetic spectra, and make recommendations for where MARCS struggles to reproduce cool dwarf fluxes. Under leave-one-out cross-validation, our \textit{Cannon} model is capable of recovering $T_{\rm eff}$, $\log g$, [Fe/H], and [Ti/Fe] with precisions of 1.4\%, $\pm0.04\,$dex, $\pm0.10\,$dex, and $\pm0.06\,$dex respectively, with the recovery of [Ti/Fe] pointing to the as-yet mostly untapped potential of exploiting the abundant -- but complex -- chemical information within optical spectra of cool stars.

Hao Cui, Yu-Yue Zhao, Qiong Wu et al.

The clinical application of cancer immunotherapy is unsatisfied due to low response rates and systemic immune-related adverse events. Microwave hyperthermia can be used as a synergistic immunotherapy to amplify the antitumor effect. Herein, we designed a Gd-based metal-organic framework (Gd-MOF) nanosystem for MRI-guided thermotherapy and synergistic immunotherapy, which featured high performance in drug loading and tumor tissue penetration. The PD-1 inhibitor (aPD-1) was initially loaded in the porous Gd-MOF (Gd/M) nanosystem. Then, the phase change material (PCM) and the cancer cell membrane were further sequentially modified on the surface of Gd/MP to obtain Gd-MOF@aPD-1@CM (Gd/MPC). When entering the tumor microenvironment (TME), Gd/MPC induces immunogenic death of tumor cells through microwave thermal responsiveness, improves tumor suppressive immune microenvironment and further enhances anti-tumor ability of T cells by releasing aPD-1. Meanwhile, Gd/MPC can be used for contrast-enhanced MRI. Transcriptomics data revealed that the downregulation of MSK2 in cancer cells leads to the downregulation of c-fos and c-jun, and ultimately leads to the apoptosis of cancer cells after treatment. In general, Gd/MPC nanosystem not only solves the problem of system side effect, but also achieves the controlled drug release via PCM, providing a promising theranostic nanoplatform for development of cancer combination immunotherapy.

Halah E. Aljofi, Thomas J. Bannan, Michael Flynn et al.

Low-cost personal exposure monitors (PEMs) to measure personal exposure to air pollution are potentially promising tools for health research. However, their adoption requires robust validation. This study evaluated the performance of twenty-one Plume Lab Flow2s (PLFs) by comparing its air pollutant measurements, particulate matter with a diameter of 2.5 μm or less (PM_2.5), 10 μm or less (PM₁₀), and nitrogen dioxide (NO₂), against several high-quality air pollution monitors under field conditions (at indoor, outdoor, and roadside locations). Correlation and regression analysis were used to evaluate measurements obtained by different PLFs against reference instrumentation. For all measured pollutants, the overall correlation coefficient between the PLFs and the reference instruments was often weak (r < 0.4). Moderate correlation was observed for one PLF unit at the indoor location and two units at the roadside location when measuring PM_2.5, but not for PM₁₀ and NO₂ concentration. During periods of particularly higher pollution, 11 PLF tools showed stronger regression results (R² values > 0.5) with one-hour and 9 PLF units with one-minute time interval. Results show that the PLF cannot be used robustly to determine high and low exposure to poor air. Therefore, the use of PLFs in research studies should be approached with caution if data quality is important to the research outputs.

Xin Meng, Fang Wang, Chao-Hong Fu et al.

Osmanthus fragrans is an evergreen shrub with a pleasant fragrance and a wide range of applications in many fields. The condensed hydrolat obtained during the drying process of its fresh flowers was collected in a low-temperature vacuum environment and its sensory evaluation and volatile components were studied. The main aroma compounds in Osmanthus fragrans were dihydro-β-ionone, nonanal, β-cyclocitral, β-ionone, benzaldehyde, α-ionone, and 6-methyl-5-hepten-2-one, whose contents were used as the main evaluation criteria, and the hydrolats obtained under different scenting and drying times were compared. This process can effectively collect the aroma components in Osmanthus fragrans and the optimal drying conditions were 50 °C for 5 h. The hydrolat was used to provide the scent of osmanthus black tea, which had a fresher and mellower taste, while the fragrance of osmanthus was abundant. These results show that osmanthus hydrolat can be used to provide the scent of floral black tea. Chemical compounds studied in this article: (−)-Catechin (PubChem CID: 1203); (−)-epigallocatechin gallate (PubChem CID: 65064); (−)-epicatechin gallate (PubChem CID: 367141); (−)-epigallocatechin (PubChem CID: 72277); (−)-epicatechin (PubChem CID: 72276); (−)-gallocatechin gallate (PubChem CID: 199472); (−)-catechin gallate (PubChem CID: 6419835); (−)-gallocatechin (PubChem CID: 9882981).

Joanna Siwek, Konrad Pierzyński, Przemysław Siwek et al.

This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applications, lack the aspect of empathy, known to improve cooperation in human teams. Emphasizing both cognitive and emotional aspects of empathy, the introduced model navigates communication uncertainties and ambiguities, transforming these challenges into opportunities for learning and adaptation in dynamic environments. A significant feature of this model is its handling of imprecision through fuzzy logic, using fuzzy similarity measures in the decision process. The main objective of the presented research is to introduce a new model for improving cooperativeness in multi-agent systems with the use of cognitive empathy. Future research focus on implementing the model on physical platform and optimize the artificial empathy algorithms in the decision-making module.

Mustapha Eddah, Henning Markötter, Björn Mieller et al.

In synchrotron X-ray radiography, achieving high image resolution and an optimal signal-to-noise ratio (SNR) is crucial for the subsequent accurate image analysis. Traditional methods often struggle to balance these two parameters, especially in situ applications where rapid data acquisition is essential to capture specific dynamic processes. For quantitative image data analysis, using monochromatic X-rays is essential. A double multilayer monochromator (DMM) is successfully used for this aim at the BAMline, BESSY II (Helmholtz Zentrum Berlin, Germany). However, such DMMs are prone to producing an unstable horizontal stripe pattern. Such an unstable pattern renders proper signal normalization difficult and thereby causes a reduction of the SNR. We introduce a novel approach to enhance SNR while preserving resolution: dynamic tilting of the DMM. By adjusting the orientation of the DMM during the acquisition of radiographic projections, we optimize the X-ray imaging quality, thereby enhancing the SNR. The corresponding shift of the projection during this movement is corrected in post-processing. The latter correction allows a good resolution to be preserved. This dynamic tilting technique enables the homogenization of the beam profile and thereby effectively reduces noise while maintaining high resolution. We demonstrate that data captured using this proposed technique can be seamlessly integrated into the existing radiographic data workflow, as it does not need hardware modifications to classical X-ray imaging beamline setups. This facilitates further image analysis and processing using established methods.

Yinan Yang, Tsukasa Hori, Shinya Sawada et al.

In addressing the demands of industrial high-fidelity computation, the present study introduces a rapid and accurate customized solver developed on the OpenFOAM platform. To enhance computational efficiency, a novel integrated acceleration strategy is introduced. Initially, a sparse analytical Jacobian approach utilizing the SpeedCHEM chemistry library was implemented to increase the efficiency of the ODE solver. Subsequently, the Dynamic Load Balancing (DLB) code was employed to uniformly distribute the computational workload for chemistry among multiple processes. Further optimization was achieved through the introduction of the Open Multi-Processing (OpenMP) method to enhance parallel computing efficiency. Lastly, the Local Time Stepping (LTS) scheme was integrated to maximize the individual time step for each computational cell, resulting in a noteworthy minimum speed-up of over 31 times. The effectiveness and robustness of this customized solver were systematically validated against three distinct partially turbulent premixed flames, Sandia Flames D, E, and F. Additionally, a comparative analysis was conducted, encompassing different turbulence models, turbulent Prandtl numbers, and model constants, resulting in the recommendation of optimal numerical parameters for various conditions. The present study offers one viable solution for rapid and accurate calculations in the OpenFOAM platform, while also providing insights into the selection of turbulence models and parameters for industrial numerical simulation.

Andreas Rosenkranz, Bo Wang, Dario Zambrano et al.

Multi-layer Ti3C2Tx coatings have demonstrated an outstanding wear performance with excellent durability due to beneficial tribo-layers formed. However, the involved formation processes dependent on the tribological conditions and coating thickness are yet to be fully explored. Therefore, we spray-coated Ti3C2Tx multi-layer particles onto stainless steel substrates to create coatings with two different thicknesses and tested their solid lubrication performance with different normal loads (100 and 200 mN) and sliding frequencies (1 and 2.4 Hz) using linear-reciprocating ball-on-disk tribometry. We demonstrate that MXenes' tribological performance depends on their initial state (delaminated few-layer vs. multi-layer particles), coating thickness, applied load and sliding frequency. Specifically, the best behavior is observed for thinner multi-layer coatings tested at the lower frequency. In contrast, coatings made of delaminated few-layer MXene are not as effective as their multi-layer counterparts. Our high-resolution interface characterization by transmission electron microscopy revealed unambiguous differences regarding the uniformity and chemistry of the formed tribo-layers as well as the degree of tribo-induced MXenes' exfoliation. Atomistic insights into the exfoliation process and molecular dynamic simulations quantitatively backed up our experimental results regarding coating thickness and velocity dependency. This ultimately demonstrates that MXenes' tribological performance is governed by the underlying tribo-chemistry and their exfoliation ability during rubbing.