Hasil untuk "Chemistry"

J. Chan, Sheel C. Dodani, Christopher J Chang

P. Power

Joseph W. Tucker, C. Stephenson

Chang-Liang Sun, Zhangjie Shi

K. Ariga, Y. Yamauchi, G. Rydzek et al.

Min Xue, Yong Yang, Xiaodong Chi et al.

Sanjanasri JP, Pratiti Bhadra, N. Sukumar et al.

Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need for molecular representations that are both machine-readable and understandable to scientists from different fields. Over the years, many chemical molecular representations have been constructed, and new ones continue to be developed as computer technology advances and knowledge of molecular complexity increases. This paper presents some of the most popular digital molecular representations inspired by natural language processing (NLP) and used in chemical informatics. In addition, the paper discusses some notable AI-based applications that use these representations. This paper aims to provide a guide to structural representations that are important for the application of AI in chemistry and materials science from the perspective of an NLP researcher. This review is a reference tool for researchers with little experience working with chemical representations who wish to work on projects at the interface of these fields.

Kun Meng, Linyan Nie, Johannes Berger et al.

Optically addressable spin systems, such as nitrogen-vacancy centers in diamond, have been widely studied for quantum sensing applications. In this work, we demonstrate that certain flavoproteins, specifically cryptochrome and iLOV, which generate spin correlated radical pairs upon optical excitation, also exhibit optically detected magnetic resonance (ODMR). Remarkably, the iLOV protein, commonly used in cellular imaging, displays ODMR contrast approaching 50%. We present initial applications including widefield magnetic field sensing and spatial modulation of photoluminescence using radiofrequency pulses and magnetic field gradients. Our results establish radical pairs in proteins as a novel platform for optically addressable spin systems, offering the key advantages of molecular designability and genetic encodability. Moreover, due to the spin-selective nature of radical pair chemistry, the results lay the groundwork for future radiofrequency-based manipulation of biological systems.

Hu Ding, Pengxiang Hua, Zhen Huang

The development of artificial intelligence (AI) techniques has brought revolutionary changes across various realms. In particular, the use of AI-assisted methods to accelerate chemical research has become a popular and rapidly growing trend, leading to numerous groundbreaking works. In this paper, we provide a comprehensive review of current AI techniques in chemistry from a computational perspective, considering various aspects in the design of methods. We begin by discussing the characteristics of data from diverse sources, followed by an overview of various representation methods. Next, we review existing models for several topical tasks in the field, and conclude by highlighting some key challenges that warrant further attention.

Jeetendra Kumar, Rashmi Gupta, Suvarna Sharma et al.

Presents corrections to the paper, (Corrections to “IoT-Enabled Advanced Water Quality Monitoring System for Pond Management and Environmental Conservation”).

An-Jun Liu, Bryan K. Clark

The ground state of second-quantized quantum chemistry Hamiltonians provides access to an important set of chemical properties. Wavefunctions based on ML architectures have shown promise in approximating these ground states in a variety of physical systems. In this work, we show how to achieve state-of-the-art energies for molecular Hamiltonians using the the neural network backflow wave-function. To accomplish this, we optimize this ansatz with a variant of the deterministic optimization scheme based on SCI introduced by [Li, et. al JCTC (2023)] which we find works better than standard MCMC sampling. For the molecules we studied, NNBF gives lower energy states than both CCSD and other neural network quantum states. We systematically explore the role of network size as well as optimization parameters in improving the energy. We find that while the number of hidden layers and determinants play a minor role in improving the energy, there is significant improvements in the energy from increasing the number of hidden units as well as the batch size used in optimization with the batch size playing a more important role.

Ahmet Emin Atik

Glycosylation is considered as a critical quality attribute for monoclonal antibodies (mAbs) and needs routine monitoring during production. This study aims to compare the glycoform profiles of biosimilar and four originator mAbs using ultra-performance liquid chromatography (UPLC) coupled to electrospray ionization-quadrupole time of flight-mass spectrometry (ESI/Q-TOF MS). The resultant mass spectrum showed that seven different glycoform pairs, including G0F–GN/G0, G0F–GN/G0F, G0F/G0F, G0F/G1F, G1F/G1F, G1F/G2F, and G2F/G2F were identified via intact mass analysis for all tested mAb samples. The correct identification of each glycoform pair was achieved by comparing the observed mass with its theoretical mass using high-resolution mass spectrometry data (with mass accuracies of less than 100 ppm). The most abundant paired glycoforms detected at the intact protein level are G0F/G0F and G0F/G1F, with relative abundance ranges of 38.45 – 43.43% and 19.32 – 22.20%, respectively. The obtained data demonstrated that biosimilar and originators have the same types of glycoform pairs, and the relative abundances of each pair were comparable among biosimilar and four originator mAb samples. Additionally, the reduced mass analysis revealed that five different glycans (G0F–GN, G0, G0F, G1F, and G2F) were attached to the heavy chain of the mAb, and the relative abundance of G0F ranged from 75.21 to 77.90%. The detected mass accuracies for reduced mass analysis were below 25 ppm. The results of the intact and reduced mass analyses showed that the biosimilar is similar to its originator in terms of glycoform percentages and molecular masses.

Mahima Kumar, Shanmugavel Chinnathambi, Noremylia Bakhori et al.

Abstract Quantum dots, which won the Nobel Prize in Chemistry, have recently gained significant attention in precision medicine due to their unique properties, such as size-tunable emission, high photostability, efficient light absorption, and vibrant luminescence. Consequently, there is a growing demand to identify new types of quantum dots from various sources and explore their potential applications as stimuli-responsive biosensors, biomolecular imaging probes, and targeted drug delivery agents. Biomass-waste-derived carbon quantum dots (CQDs) are an attractive alternative to conventional QDs, which often require expensive and toxic precursors, as they offer several merits in eco-friendly synthesis, preparation from renewable sources, and cost-effective production. In this study, we evaluated three CQDs derived from biomass waste for their potential application as non-toxic bioimaging agents in various cell lines, including human dermal fibroblasts, HeLa, cardiomyocytes, induced pluripotent stem cells, and an in-vivo medaka fish (Oryzias latipes) model. Confocal microscopic studies revealed that CQDs could assist in visualizing inflammatory processes in the cells, as they were taken up more by cells treated with tumor necrosis factor-α than untreated cells. In addition, our quantitative real-time PCR gene expression analysis has revealed that citric acid-based CQDs can potentially reduce inflammatory markers such as Interleukin-6. Our studies suggest that CQDs have potential as theragnostic agents, which can simultaneously identify and modulate inflammatory markers and may lead to targeted therapy for immune system-associated diseases.

Maknunah Hilyatul, Wonorahardjo Surjani

Sensors play a crucial role in various fields by enabling the detection and analysis of a wide range of substances, including hazardous substance detection, environmental and food safety monitoring, pharmaceutical industry, gas analysis, and others. Research continues to identify and develop sensor matrix materials that can increase the sensitivity, selectivity and responsiveness of sensors. Silica, an oxide mineral is a potential matrix material for sensor applications because of its unique characteristics. It has a large pore structure and modifiable pore size distribution. Silica’s stable chemical properties, high-temperature resistance and corrosion resistance make it an ideal matrix material for a wide range of sensor applications. In recent years, silica cellulose also become a potential material for sensor applications. Silica cellulose is produced by combining silica with cellulose components from natural materials, such as rice husk ash, bamboo leaf ash, rice straw ash, and other plant fibers. This article provides a comprehensive exploration of various methods of synthesis and characterization of silica and silica cellulose materials. The methods include sol-gel, acid leaching, alkaline extraction, and other techniques for extracting cellulose from natural sources. In addition, sensor applications that have been tested using this material are also discussed, including its use in detecting molecular compounds, food and environmental applications. The development of silica and silica cellulose materials based on natural materials is considered because of their sustainability. By continuing to explore the potential of these materials, it is hoped that it can make a significant contribution in the development of sensor technology that is more innovative, environmentally friendly and sustainable.

Surojit Sural, Juan Quintero Botero, Oliver Hobert et al.

Summary: The auxin-inducible degron (AID) system is a broadly used tool for spatiotemporal and reversible control of protein depletion in multiple experimental model systems. AID2 technology relies on a synthetic ligand, 5-phenyl-indole-3-acetic acid (5-Ph-IAA), for improved specificity and efficiency of protein degradation. Here, we provide a protocol for cost-effective 5-Ph-IAA synthesis utilizing the Suzuki coupling of 5-chloroindole and phenylboronic acid. We describe steps for evaluating the quality of lab-synthesized 5-Ph-IAA using a C. elegans AID2 tester strain. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Xiaomeng Deng, Wensheng Wei, Zizhen Yan et al.

A meaningful and practical phenomenon was observed and investigated, about which a synthesized (2-hydroxyethyl) triethylammonium bromide (NEt3(HE)Br) was used for catalytic cycloaddition of PO (propylene oxide) and CO2 (carbon dioxide) in a homogeneous state, and then separated from the reaction system in a heterogeneous state. The target products were first reported as additives to regulate the distribution of reactants and carbon dioxide (CO2) in the gas-liquid phase, as well as to facilitate the dissolution of NEt3(HE)Br. Under the optimal conditions of 0.2 mol% NEt3(HE)Br dosage, the PC yield (98.71 %) and TOF value (22.54 h−1) reached are significantly higher than the case of without PC addition. Besides, NEt3(HE)Br exhibited impressive stability after five catalytic cycles. The catalytic performances of different additives (i.e., alcohols, cyclic carbonate, amides, sulfones, petroleum ether, straight-chain carbonate and oxalate) were investigated, and their promotional effects and mechanisms were systematically elucidated. The transition behavior of dissolution-catalysis-precipitation process and intrinsic promoting mechanism of NEt3(HE)Br gave an efficient idea for a homogeneous catalytic reaction and heterogeneous separation of the catalyst. Meanwhile, this work provides an important theoretical and practical basis for efficient and facile catalyst synthesis and the corresponding reaction system design.

Werner Dobrautz, Igor O. Sokolov, Ke Liao et al.

Quantum computing is emerging as a new computational paradigm with the potential to transform several research fields, including quantum chemistry. However, current hardware limitations (including limited coherence times, gate infidelities, and limited connectivity) hamper the straightforward implementation of most quantum algorithms and call for more noise-resilient solutions. In quantum chemistry, the limited number of available qubits and gate operations is particularly restrictive since, for each molecular orbital, one needs, in general, two qubits. In this study, we propose an explicitly correlated Ansatz based on the transcorrelated (TC) approach, which transfers -- without any approximation -- correlation from the wavefunction directly into the Hamiltonian, thus reducing the number of resources needed to achieve accurate results with noisy, near-term quantum devices. In particular, we show that the exact transcorrelated approach not only allows for more shallow circuits but also improves the convergence towards the so-called basis set limit, providing energies within chemical accuracy to experiment with smaller basis sets and, therefore, fewer qubits. We demonstrate our method by computing bond lengths, dissociation energies, and vibrational frequencies close to experimental results for the hydrogen dimer and lithium hydride using just 4 and 6 qubits, respectively. Conventional methods require at least ten times more qubits for the same accuracy.

Ruhee D'Cunha, Matthew Otten, Matthew R. Hermes et al.

State preparation for quantum algorithms is crucial for achieving high accuracy in quantum chemistry and competing with classical algorithms. The localized active space unitary coupled cluster (LAS-UCC) algorithm iteratively loads a fragment-based multireference wave function onto a quantum computer. In this study, we compare two state preparation methods, quantum phase estimation (QPE) and direct initialization (DI), for each fragment. We analyze the impact of QPE parameters, such as the number of ancilla qubits and Trotter steps, on the prepared state. We find a trade-off between the methods, where DI requires fewer resources for smaller fragments, while QPE is more efficient for larger fragments. Our resource estimates highlight the benefits of system fragmentation in state preparation for subsequent quantum chemical calculations. These findings have broad applications for preparing multireference quantum chemical wave functions on quantum circuits, particularly via QPE circuits.

Eleonora Bianchi, Anthony Remijan, Claudio Codella et al.

We report a comprehensive study of the cyanopolyyne chemistry in the prototypical prestellar core L1544. Using the 100m Robert C. Byrd Green Bank Telescope (GBT) we observe 3 emission lines of HC$_3$N, 9 lines of HC$_5$N, 5 lines of HC$_7$N, and 9 lines of HC$_9$N. HC$_9$N is detected for the first time towards the source. The high spectral resolution ($\sim$ 0.05 km s$^{-1}$) reveals double-peak spectral line profiles with the redshifted peak a factor 3-5 brighter. Resolved maps of the core in other molecular tracers indicates that the southern region is redshifted. Therefore, the bulk of the cyanopolyyne emission is likely associated with the southern region of the core, where free carbon atoms are available to form long chains, thanks to the more efficient illumination of the interstellar field radiation. We perform a simultaneous modelling of the HC$_5$N, HC$_7$N, and HC$_9$N lines, to investigate the origin of the emission. To enable this analysis, we performed new calculation of the collisional coefficients. The simultaneous fitting indicates a gas kinetic temperature of 5--12 K, a source size of 80$\arcsec$, and a gas density larger than 100 cm$^{-3}$. The HC$_5$N:HC$_7$N:HC$_9$N abundance ratios measured in L1544 are about 1:6:4. We compare our observations with those towards the the well-studied starless core TMC-1 and with the available measurements in different star-forming regions. The comparison suggests that a complex carbon chain chemistry is active in other sources and it is related to the presence of free gaseous carbon. Finally, we discuss the possible formation and destruction routes in the light of the new observations.