Chemical Applications Research Articles

Tabasheer : A Comprehensive Study of Traditional Uses, Phytochemical, Biochemical and Medicinal properties of Tabasheer in Different Varieties of Bamboo

Tabasheer also known as "Bamboo Manna" is a unique substance that has been used in traditional medicine and various industries for centuries. This review paper provides a comprehensive review of Tabasheer including it's a sources chemical composition and potential applications. The paper aims consolidated the existing knowledge on Tabasheer and shed light on its significance in various fields including Pharmaceutical cosmetic and Material Science. Additionally this review discusses the challenges and further properties associated with Tabasheer.

Optical Fiber Bragg Grating As A Temperature Sensor

The optical fibre sensor (OFS) has become quite well-known and prominent in the sensor technology industry. It is often used to react to outputs from other systems, like those in industrial, monitoring, and chemical analysis applications, and to detect changes in the environment. A Fibre Bragg Grating (FBG) is a device that allows light to be reflected from a short section of optical fiber at a specific wavelength, while the Bragg reflector expands and transmits all other wavelengths. The current effort focuses on the evolving characteristics and behaviors of strain and temperature sensors operating on fiber bragg gratings through computer modeling. The temperature sensor is employed to detect and quantify any variations in temperature on the Fiber Bragg Grating that could result in accidents or fires. This will show how the Fibre Bragg Grating may be used to showcase temperature sensors.

Peran Ilmu Kimia dalam Kehidupan Sehari-hari Menurut Perspektif Santri Puteri Pondok Pesantren Al-Ihsan Banjarmasin

This research aims to explore the role of chemistry in daily life from the perspective of female students (santri puteri) at Al-Ihsan Islamic Boarding School in Banjarmasin. Through qualitative methods including interviews and observations, this study investigates the understanding, perceptions, and experiences of the students regarding the practical applications of chemistry in their everyday lives. The findings highlight the significance of integrating chemistry education with real-life contexts to enhance students' comprehension and appreciation of this scientific discipline. Understanding the perspectives of female students in Islamic boarding schools sheds light on how chemistry education can be tailored to meet the needs and interests of diverse learner populations.

Copper-catalyzed three-component reaction to construct thietane ring using elemental sulfur

Thietanes are vital aliphatic four-membered rings associated with various pharmaceuticals, building blocks and synthons of intermediates used in the total synthesis of natural products and biomolecules. Also, 4-thiazolidinone is a core pharmacophore of many drug molecules. Based on their various applications in medicinal chemistry and organic synthesis, an effort has been made to achieve a thietane ring on 4-thiazolidinone nucleus via quasi-benzylic acid-type intramolecular rearrangement using elemental sulfur, benzyl bromide and Cu(I). The designed methodology is environmentally benign, cheap, and easily accessible, with a broad range of substrate scopes and carried out by the employment of earth-abundant metal catalysts like copper.

Sherwood (Sh) Number in Chemical Engineering Applications—A Brief Review

Sherwood (Sh) Number in Chemical Engineering Applications—A Brief Review

Physicochemical Characteristics of Titania Particles Synthesized with Gelatin as a Template Before and After Regeneration and Their Performance in Photocatalytic Methylene Blue

TiO2 material has an important position in the processing of methylene blue waste because it is economical, has abundant polymorphs, high sustainability and supports green chemistry applications. Mesoporous TiO2 is a porous material that has higher effectiveness than other TiO2 because the pore structure has a large diameter at the nano scale (2-50 nm) with a regular shape so that the surface area and pore volume are greater than the average for other TiO2. The synthesis of mesopore TiO2 material has so far used the sol-gel route with synthetic pore directing agents such as P123 which can be replaced with gelatin as a cheaper and safer pore directing agent with high sustainability and abundance. Based on the description above, this research aims to photodegrade methylene blue using mesoporous TiO2 (m-TiO2) nanoparticles which were prepared by the sol–gel method using gelatin and P123 as template. X-ray diffraction (XRD), scanning electron microscopy (SEM), Electron dispersive X-Ray (EDX), and UV–vis spectroscopy techniques were used to characterize the samples. Photocatalytic activities of samples for methylene blue degradation were investigated. The catalyst before and after regeneration will be studied so that the effect of regeneration on the results of methylene blue photocatalysis with m-TiO2 can be determined. XRD results confirmed the formation of the anatase and rutile phase for the TiO2 nanoparticles, with crystallite sizes larger after regeneration in the range of 9–21 nm. The large particle size was after regeneration due to the promotion by high temperature treatment. TiO2 nanoparticles showed the best photocatalytic activity on the first use to 91% and remained stable after four cycles with photodegradation efficiency up to 76% based on the measured UV-Vis spectroscopy. TiO2 as synthesis could be the best candidate for catalyst with the high performance after multicycle regeneration. Copyright © 2024 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery.

In the past several years there has been rapid adoption of artificial intelligence (AI) and machine learning (ML) tools for drug discovery. In this Microperspective, we comment on recent AI/ML applications to the discovery of allosteric modulators, focusing on breakthroughs with AlphaFold, structure-based drug discovery (SBDD), and medicinal chemistry applications. We discuss how these technologies are facilitating drug discovery and the remaining challenges to identify allosteric binding sites and ligands.

Learning integral operators via neural integral equations

Nonlinear operators with long-distance spatiotemporal dependencies are fundamental in modelling complex systems across sciences; yet, learning these non-local operators remains challenging in machine learning. Integral equations, which model such non-local systems, have wide-ranging applications in physics, chemistry, biology and engineering. We introduce the neural integral equation, a method for learning unknown integral operators from data using an integral equation solver. To improve scalability and model capacity, we also present the attentional neural integral equation, which replaces the integral with self-attention. Both models are grounded in the theory of second-kind integral equations, where the indeterminate appears both inside and outside the integral operator. We provide a theoretical analysis showing how self-attention can approximate integral operators under mild regularity assumptions, further deepening previously reported connections between transformers and integration, as well as deriving corresponding approximation results for integral operators. Through numerical benchmarks on synthetic and real-world data, including Lotka–Volterra, Navier–Stokes and Burgers’ equations, as well as brain dynamics and integral equations, we showcase the models’ capabilities and their ability to derive interpretable dynamics embeddings. Our experiments demonstrate that attentional neural integral equations outperform existing methods, especially for longer time intervals and higher-dimensional problems. Our work addresses a critical gap in machine learning for non-local operators and offers a powerful tool for studying unknown complex systems with long-range dependencies.

Electrocatalytic reductive deuteration of arenes and heteroarenes.

The incorporation of deuterium in organic molecules has widespread applications in medicinal chemistry and materials science1,2. For example, the deuterated drugs austedo3, donafenib4 and sotyktu5 have been recently approved. There are various methods for the synthesis of deuterated compounds with high deuterium incorporation6. However, the reductive deuteration of aromatic hydrocarbons-ubiquitous chemical feedstocks-to saturated cyclic compounds has rarely been achieved. Here we describe a scalable and general electrocatalytic method for the reductive deuteration and deuterodefluorination of (hetero)arenes using a prepared nitrogen-doped electrode and deuterium oxide (D2O), giving perdeuterated and saturated deuterocarbon products. This protocol has been successfully applied to the synthesis of 13 highly deuterated drug molecules. Mechanistic investigations suggest that the ruthenium-deuterium species, generated by electrolysis of D2O in the presence of a nitrogen-doped ruthenium electrode, are key intermediates that directly reduce aromatic compounds. This quick and cost-effective methodology for the preparation of highly deuterium-labelled saturated (hetero)cyclic compounds could be applied in drug development and metabolism studies.

Good results from sensor data: Performance of machine learning algorithms for regression problems in chemical sensors

Accurately predicting unseen data, instead of mere memorization of training examples, is a critical goal of machine learning. This generalization is particularly important in the field of chemical sensors, where the ability to accurately predict the chemical properties or concentration levels of unknown samples is crucial. The paper presents a comprehensive yet accessible introduction to various machine learning concepts, highlighting the importance of model interpretability and generalization in ensuring reliable and accurate results in this context. Nonlinear sensor array data are utilized to introduce key concepts (e.g., bias-variance tradeoff) and techniques (linear models, partial least squares regression, support vector machines, k-nearest neighbors, decision trees, ensemble methods, automated machine learning, symbolic regression, and artificial neural networks), providing a solid foundation to make informed decisions when selecting machine learning techniques for sensor-specific regression applications. The results clearly indicate a number of conclusions. First, overparameterized deep feedforward neural networks show great accuracy and generalization when trained on a sufficiently large dataset. Second, symbolic regression models proved to be more accurate than deep feedforward neural networks and classical machine learning techniques on smaller datasets. Third, the performance of various machine learning models was dataset-dependent, showing the importance of comparative studies to determine the most suitable approach. It is clear that the optimal model cannot be known a priori. This paper aims to provide a starting point for investigations on the performance of different machine learning techniques in chemical sensor applications.

C1‐4 Alkylation of Aryl Bromides with Light Alkanes enabled by Metallaphotocatalysis in Flow

The homologous series of gaseous C1‐4 alkanes represents one of the most abundant sources of short alkyl fragments. However, their application in synthetic organic chemistry is exceedingly rare due to the challenging C–H bond cleavage, which typically demands high temperatures and pressures, thereby limiting their utility in the construction of complex organic molecules. In particular, the formation of C(sp2)–C(sp3) bonds is crucial for constructing biologically active molecules, including pharmaceuticals and agrochemicals. In this study, we present the previously elusive coupling between gaseous alkanes and (hetero)aryl bromides, achieved through a combination of Hydrogen Atom Transfer (HAT) photocatalysis and nickel‐catalyzed cross coupling at room temperature. Utilizing flow technology allowed us to conduct this novel coupling reaction with reduced reaction times and in a scalable fashion, rendering it practical for widespread adoption in both academia and industry. Density Functional Theory (DFT) calculations unveiled that the oxidative addition constitutes the rate‐determining step, with the activation energy barrier increasing with smaller alkyl radicals. Furthermore, radical isomerization observed in propane and butane analogues could be attributed to the electronic properties of the bromoarene coupling partner, highlighting the crucial role of oxidative addition in the observed selectivity of this transformation.

C1-4 Alkylation of Aryl Bromides with Light Alkanes enabled by Metallaphotocatalysis in Flow.

The homologous series of gaseous C1-4 alkanes represents one of the most abundant sources of short alkyl fragments. However, their application in synthetic organic chemistry is exceedingly rare due to the challenging C-H bond cleavage, which typically demands high temperatures and pressures, thereby limiting their utility in the construction of complex organic molecules. In particular, the formation of C(sp2)-C(sp3) bonds is crucial for constructing biologically active molecules, including pharmaceuticals and agrochemicals. In this study, we present the previously elusive coupling between gaseous alkanes and (hetero)aryl bromides, achieved through a combination of Hydrogen Atom Transfer (HAT) photocatalysis and nickel-catalyzed cross coupling at room temperature. Utilizing flow technology allowed us to conduct this novel coupling reaction with reduced reaction times and in a scalable fashion, rendering it practical for widespread adoption in both academia and industry. Density Functional Theory (DFT) calculations unveiled that the oxidative addition constitutes the rate-determining step, with the activation energy barrier increasing with smaller alkyl radicals. Furthermore, radical isomerization observed in propane and butane analogues could be attributed to the electronic properties of the bromoarene coupling partner, highlighting the crucial role of oxidative addition in the observed selectivity of this transformation.

Catalytic Atroposelective Synthesis of N-N Axially Chiral Indolylamides.

The catalytic atroposelective synthesis of N-N axially chiral indolylamides was established via dynamic kinetic resolution, which makes use of chiral Lewis base-catalyzed asymmetric acylation of N-acylaminoindoles as a new type of platform molecule with anhydrides. By this strategy, a series of N-N axially chiral indolylamides were synthesized in overall good yields (up to 98%) with excellent enantioselectivities (up to 99% ee). Moreover, some of these N-N axially chiral indolylamides display some extent of anticancer activity, which demonstrates their potential application in medicinal chemistry. Therefore, this work has not only provided a new strategy for the synthesis of N-N axially chiral monoaryl indoles but also offered a new member of N-N axially chiral monoaryl indoles with configurational stability and promising application, thereby solving the challenges in atroposelective synthesis and application of N-N axially chiral monoaryl indoles.

Factors influencing the performance of organocatalysts immobilised on solid supports: A review.

Organocatalysis has become a powerful tool in synthetic chemistry, providing a cost-effective alternative to traditional catalytic methods. The immobilisation of organocatalysts offers the potential to increase catalyst reusability and efficiency in organic reactions. This article reviews the key parameters that influence the effectiveness of immobilised organocatalysts, including the type of support, immobilisation techniques and the resulting interactions. In addition, the influence of these factors on catalytic activity, selectivity and recyclability is discussed, providing an insight into optimising the performance of immobilised organocatalysts for practical applications in organic chemistry.

Highly universal sensitive star core photonic crystal fiber (S-PCF)-based sensor for both chemical and biomedical applications

Highly universal sensitive star core photonic crystal fiber (S-PCF)-based sensor for both chemical and biomedical applications

Structural and Electronic Factors Controlling the Efficiency and Rate of Intersystem Crossing to the Triplet State in Thiophene Polycyclic Derivatives.

Thiophene polycyclic derivatives are widely used in organic light-emitting diodes, photovoltaics, and medicinal chemistry applications. Understanding the electronic and structural factors controlling their intersystem crossing rates is paramount for these applications to be successful. This study investigates the photophysical, electronic structure, and excited state dynamics of 1,2-benzodiphenylene sulfide, benzo[b]naphtho[1,2-d]thiophene, and benzo[b]naphtho[2,3-d]thiophene in polar aprotic and non-polar solvents. Steady-state absorption and emission spectroscopy, femtosecond transient absorption spectroscopy, and DFT and TD-DFT calculations are employed. Low fluorescence quantum yields of 1.2 to 2.7% are measured in acetonitrile and cyclohexene, evidencing that the primary relaxation pathways in these thiophene derivatives are nonradiative. Linear interpolation of internal coordinates calculations predict that an S-C bond elongation reaction coordinate facilitates the efficient intersystem crossing to the T1 state. Excitation of 1,2-benzodiphenylene sulfide and benzo[b]naphtho[1,2-d]thiophene at 350 nm or benzo[b]naphtho[2,3-d]thiophene at 365 nm, populates the lowest-energy 1ππ* state, which relaxes to the 1ππ* minimum in tens of picoseconds or intersystem crosses to the triplet manifold in ca. 500 ps to 1.1 ns depending on the position at which the benzene rings are added. Excitation at 266 nm does not affect the intersystem crossing rates. Laser photodegradation experiments demonstrate that the thiophene polycyclic derivatives are highly photostable.

Bubble behavior parameters extraction and analysis during pool boiling based on deep-learning method

The nucleate pool boiling plays an important role in thermal and chemical engineering applications. Analyzing bubble dynamics at nucleation site is crucial to improve the understanding of boiling heat transfer mechanism. Quantitative extraction of bubble parameters from high-speed visualized images is a labor-intensitive and time-consuming task making it necessary for automatically detect single bubble growth and measure boiling characteristic parameters.In the present work, we proposed a deep learning based self-adaptive statistical algorithm for extraction of bubble behavior parameters quickly and automatically from numerous high-speed visualization images looking from the side view of a boiling chamber. A dataset was constructed for training and performance evaluation based on experimental data of saline solution pool boiling. The StarDist and U-Net convolutional neural network were combined in the algorithm so that more exact segmentation of the bubbles can be identified. Based on the segmentation results, a post-processing program was developed to extract the sequential variation of bubbles during consecutive cycles at nucleation sites. The dynamic characteristic parameters that affect heat transfer, such as nucleation density, bubble departure diameter, departure frequency, and wait time under different heat flux were obtained quantitatively. The comparison of automatic extraction algorithm and manual processing proves the reliability and superiority of our method. This work indicates that the proposed method has great potential to be widely applied as an efficient and universal tool for processing different types of bubble shadowgraph images.

Analyzing topological descriptors of guar gum and its derivatives for predicting physical properties in carbohydrates

Guar gum is a non-ionic polysaccharide found in abundance in nature. It may be used as a thickening agent, stabilizer, or emulsifier in pharmaceutical formulations, food products, or cosmetics. Its ability to form viscous solutions makes it useful in drug delivery systems, controlled release formulations, and as a matrix for oral drug delivery. The investigation of chemical structures through graph invariants is of great concern. Topological descriptors are numerical numbers associated with the molecular structure and have the ability to predict certain physical and chemical properties of the underlying structure. In this paper, we have calculated the harmonic index, the inverse sum indeg index, the third Zagreb index, the Hyper Zagreb index, the sigma index, the reformulated first Zagreb index, the reformulated multiplicative first Zagreb index, the Harmonic–arithmetic index, and the Atom Bond sum connectivity indices of guar gum and its chemical derivatives. Finally, the chemical applicability of these topological descriptors is checked for different carbohydrates (monosaccharides, disaccharides, and polysaccharides) by using straight-line, parabolic and logarithmic regression models. It has been observed that these topological descriptors are useful to predict two physical properties, namely density and molecular weight.

Kekulé Counts, Clar Numbers, and ZZ Polynomials for All Isomers of (5,6)-Fullerenes C52-C70.

We report an extensive tabulation of several important topological invariants for all the isomers of carbon (5,6)-fullerenes Cn with n = 52-70. The topological invariants (including Kekulé count, Clar count, and Clar number) are computed and reported in the form of the corresponding Zhang-Zhang (ZZ) polynomials. The ZZ polynomials appear to be distinct for each isomer cage, providing a unique label that allows for differentiation between various isomers. Several chemical applications of the computed invariants are reported. The results suggest rather weak correlation between the Kekulé count, Clar count, Clar number invariants, and isomer stability, calling into doubt the predictive power of these topological invariants in discriminating the most stable isomer of a given fullerene. The only exception is the Clar count/Kekulé count ratio, which seems to be the most important diagnostic discovered from our analysis. Stronger correlations are detected between Pauling bond orders computed from Kekulé structures (or Clar covers) and the corresponding equilibrium bond lengths determined from the optimized DFTB geometries of all 30,579 isomers of C20-C70.

Attosecond electron microscopy and diffraction.

Advances in attosecond spectroscopy have enabled tracing and controlling the electron motion dynamics in matter, although they have yielded insufficient information about the electron dynamic in the space domain. Hence, ultrafast electron and x-ray imaging tools have been developed to image the ultrafast dynamics of matter in real time and space. The cutting-edge temporal resolution of these imaging tools is on the order of a few tens to a hundred femtoseconds, limiting imaging to the atomic dynamics and leaving electron motion imaging out of reach. Here, we obtained the attosecond temporal resolution in the transmission electron microscope, which we coined "attomicroscopy." We demonstrated this resolution by the attosecond diffraction measurements of the field-driven electron dynamics in graphene. This attosecond imaging tool would provide more insights into electron motion and directly connect it to the structural dynamics of matter in real-time and space domains, opening the door for long-anticipated real-life attosecond science applications in quantum physics, chemistry, and biology.