Chemistry Program Research Articles

Scalable Preparation of 1‐Aminoethyl Substituted Chromanes

AbstractChromanes and chromanones are structural motives found in many bioactive compounds. To support structure‐activity‐relationship (SAR) studies in a medicinal chemistry program, we developed two alternative synthetic strategies to access 1‐aminoethyl chromane building blocks. Our strategies involve a C(sp2)−C(sp3) coupling for late‐stage introduction of the aminoethyl side chain. In addition, an optimized process was developed to enable decagram scale preparation of one building block.

Scalable Preparation of 1‐Aminoethyl Oxindoles From Simple Benzaldehydes

AbstractOxindoles are prevalent structures in natural products and pharmaceutically active molecules. To support structure–activity‐relationship (SAR) studies in a medicinal chemistry program, we developed a straightforward and scalable synthesis route to 1‐aminoethyl oxindole building blocks harboring various types of substituents. Our strategy relies on an intramolecular Buchwald–Hartwig amidation of a 2‐bromophenylacetic amide precursor. The cyclization substrates can be prepared from readily available benzaldehydes by ortho‐selective C(sp2)−H bromination followed by homologation to the corresponding phenylacetic acid derivatives. The process was optimized to allow for preparation of 246 g of one representative example.

A monoacylglycerol lipase inhibitor showing therapeutic efficacy in mice without central side effects or dependence

Monoacylglycerol lipase (MAGL) regulates endocannabinoid 2-arachidonoylglycerol (2-AG) and eicosanoid signalling. MAGL inhibition provides therapeutic opportunities but clinical potential is limited by central nervous system (CNS)-mediated side effects. Here, we report the discovery of LEI-515, a peripherally restricted, reversible MAGL inhibitor, using high throughput screening and a medicinal chemistry programme. LEI-515 increased 2-AG levels in peripheral organs, but not mouse brain. LEI-515 attenuated liver necrosis, oxidative stress and inflammation in a CCl4-induced acute liver injury model. LEI-515 suppressed chemotherapy-induced neuropathic nociception in mice without inducing cardinal signs of CB1 activation. Antinociceptive efficacy of LEI-515 was blocked by CB2, but not CB1, antagonists. The CB1 antagonist rimonabant precipitated signs of physical dependence in mice treated chronically with a global MAGL inhibitor (JZL184), and an orthosteric cannabinoid agonist (WIN55,212-2), but not with LEI-515. Our data support targeting peripheral MAGL as a promising therapeutic strategy for developing safe and effective anti-inflammatory and analgesic agents.

Long-term mortality burden trends attributed to black carbon and PM2·5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study

Long-term improvements in air quality and public health in the continental USA were disrupted over the past decade by increased fire emissions that potentially offset the decrease in anthropogenic emissions. This study aims to estimate trends in black carbon and PM2·5 concentrations and their attributable mortality burden across the USA. In this study, we derived daily concentrations of PM2·5 and its highly toxic black carbon component at a 1-km resolution in the USA from 2000 to 2020 via deep learning that integrated big data from satellites, models, and surface observations. We estimated the annual PM2·5-attributable and black carbon-attributable mortality burden at each 1-km2 grid using concentration-response functions collected from a national cohort study and a meta-analysis study, respectively. We investigated the spatiotemporal linear-regressed trends in PM2·5 and black carbon pollution and their associated premature deaths from 2000 to 2020, and the impact of wildfires on air quality and public health. Our results showed that PM2·5 and black carbon estimates are reliable, with sample-based cross-validated coefficients of determination of 0·82 and 0·80, respectively, for daily estimates (0·97 and 0·95 for monthly estimates). Both PM2·5 and black carbon in the USA showed significantly decreasing trends overall during 2000 to 2020 (22% decrease for PM2·5 and 11% decrease for black carbon), leading to a reduction of around 4200 premature deaths per year (95% CI 2960-5050). However, since 2010, the decreasing trends of fine particles and premature deaths have reversed to increase in the western USA (55% increase in PM2·5, 86% increase in black carbon, and increase of 670 premature deaths [460-810]), while remaining mostly unchanged in the eastern USA. The western USA showed large interannual fluctuations that were attributable to the increasing incidence of wildfires. Furthermore, the black carbon-to-PM2·5 mass ratio increased annually by 2·4% across the USA, mainly due to increasing wildfire emissions in the western USA and more rapid reductions of other components in the eastern USA, suggesting a potential increase in the relative toxicity of PM2·5. 100% of populated areas in the USA have experienced at least one day of PM2·5 pollution exceeding the daily air quality guideline level of 15 μg/m3 during 2000-2020, with 99% experiencing at least 7 days and 85% experiencing at least 30 days. The recent widespread wildfires have greatly increased the daily exposure risks in the western USA, and have also impacted the midwestern USA due to the long-range transport of smoke. Wildfires have become increasingly intensive and frequent in the western USA, resulting in a significant increase in smoke-related emissions in populated areas. This increase is likely to have contributed to a decline in air quality and an increase in attributable mortality. Reducing fire risk via effective policies besides mitigation of climate warming, such as wildfire prevention and management, forest restoration, and new revenue generation, could substantially improve air quality and public health in the coming decades. National Aeronautics and Space Administration (NASA) Applied Science programme, NASA MODIS maintenance programme, NASA MAIA satellite mission programme, NASA GMAO core fund, National Oceanic and Atmospheric Administration (NOAA) GEO-XO project, NOAA Atmospheric Chemistry, Carbon Cycle, and Climate (AC4) programme, and NOAA Educational Partnership Program with Minority Serving Institutions.

Discovery of a Drug-like, Natural Product-Inspired DCAF11 Ligand Chemotype

Targeted proteasomal and autophagic protein degradation, often employing bifunctional modalities, is a new paradigm for modulation of protein function. In an attempt to explore protein degradation by means of autophagy we combine arylidene-indolinones reported to bind the autophagy-related LC3B-protein and ligands of the PDEδ lipoprotein chaperone, the BRD2/3/4-bromodomain containing proteins and the BTK- and BLK kinases. Unexpectedly, the resulting bifunctional degraders do not induce protein degradation by means of macroautophagy, but instead direct their targets to the ubiquitin-proteasome system. Target and mechanism identification reveal that the arylidene-indolinones covalently bind DCAF11, a substrate receptor in the CUL4A/B-RBX1-DDB1-DCAF11 E3 ligase. The tempered α, β-unsaturated indolinone electrophiles define a drug-like DCAF11-ligand class that enables exploration of this E3 ligase in chemical biology and medicinal chemistry programs. The arylidene-indolinone scaffold frequently occurs in natural products which raises the question whether E3 ligand classes can be found more widely among natural products and related compounds.

Electrochemistry education in the twenty-first century: the current landscape in the UK, challenges and opportunities

Electrochemistry education of future researchers is crucial if we are to decarbonise economies and reach targets for net zero, and this arguably begins with education in electrochemistry within undergraduate degrees. This paper reviews the teaching of electrochemistry in UK universities at the undergraduate degree level. We review where and how electrochemical concepts are introduced into chemistry, chemical engineering and materials science programmes. We provide some motivation for this review, which was stimulated by discussions from a workshop on the ‘Future of Fundamental Electrochemistry Research in the UK’, held in 2022. We summarise briefly how consensus on UK degree programme course content has been reached and inconsistencies that remain. Electrochemistry curriculum content from a convenience sample of UK universities, and disciplines, has been collected and is summarised, with a reflection on some trends. Finally, we present some implications for policy. A roadmap is suggested to ensure that the teaching of electrochemical fundamentals is addressed in the curriculum at an appropriate level to underpin the many technically relevant applications of electrochemistry that graduates will encounter in their further education or employment.

Development and Perception of Trainee Teachers on Salt-UNO Card Game for Salt Topic

Game-based learning is an effective teaching strategy to improve students’ academics, increase motivation and involve students more actively during learning and facilitation. This study was conducted to develop Salt-UNO Card game for Form Four Salt topic. This study also aims to determine the value of game content validity, game feature validity and game perception from the aspects of design, content and usability. The research design of this study is developmental study. Instructional design model used to develop Salt-UNO Card game is the ADDIE Model. The instruments used in this study are game content validity evaluation form, game feature validity evaluation form and game perception questionnaire. A total of two chemistry lecturers were appointed to assess the validity of the game content and two game field lecturers were appointed to assess the validity of the game feature. The respondents for the pilot study were 31 semester seven trainee teachers of the Bachelor of Education (Chemistry) programme. A total of 201 semester one until semester six trainee teachers of the Bachelor of Education (Chemistry) programme were involved in the field study to identify the perception of the game. Cohen kappa value for content validity of the game is 1.00 and the validity of the game feature is 0.84. Field study findings show that trainee teachers' perceptions of game design, game content and game usability is good. In conclusion, the Salt-UNO Card game has a satisfactory values of game content validity and game feature validity. Perceptions of the game also gained good feedback from the trainee teachers. Implication of the study is the use of Salt-UNO Card game during the learning and facilitation process is very suitable and useful to help students in the learning of Chemistry subject especially in Salt topic.

Machine Learning Approaches Identify Chemical Features for Stage-Specific Antimalarial Compounds.

Efficacy data from diverse chemical libraries, screened against the various stages of the malaria parasite Plasmodium falciparum, including asexual blood stage (ABS) parasites and transmissible gametocytes, serve as a valuable reservoir of information on the chemical space of compounds that are either active (or not) against the parasite. We postulated that this data can be mined to define chemical features associated with the sole ABS activity and/or those that provide additional life cycle activity profiles like gametocytocidal activity. Additionally, this information could provide chemical features associated with inactive compounds, which could eliminate any future unnecessary screening of similar chemical analogs. Therefore, we aimed to use machine learning to identify the chemical space associated with stage-specific antimalarial activity. We collected data from various chemical libraries that were screened against the asexual (126 374 compounds) and sexual (gametocyte) stages of the parasite (93 941 compounds), calculated the compounds' molecular fingerprints, and trained machine learning models to recognize stage-specific active and inactive compounds. We were able to build several models that predict compound activity against ABS and dual activity against ABS and gametocytes, with Support Vector Machines (SVM) showing superior abilities with high recall (90 and 66%) and low false-positive predictions (15 and 1%). This allowed the identification of chemical features enriched in active and inactive populations, an important outcome that could be mined for essential chemical features to streamline hit-to-lead optimization strategies of antimalarial candidates. The predictive capabilities of the models held true in diverse chemical spaces, indicating that the ML models are therefore robust and can serve as a prioritization tool to drive and guide phenotypic screening and medicinal chemistry programs.

The Cygnus Allscale Survey of Chemistry and Dynamical Environments: CASCADE

Context.Molecular outflows are believed to be a key ingredient in the process of star formation. The molecular outflow associated with DR21 Main in Cygnus-X is one of the most extreme molecular outflows in the Milky Way in terms of mass and size. The outflow is suggested to belong to a rare class of explosive outflows formed by the disintegration of protostellar systems.Aims.We aim to explore the morphology, kinematics, and energetics of the DR21 Main outflow, and to compare those properties to confirmed explosive outflows in order to unravel the underlying driving mechanism behind DR21.Methods.We studied line and continuum emission at a wavelength of 3.6 mm with IRAM 30 m and NOEMA telescopes as part of the Cygnus Allscale Survey of Chemistry and Dynamical Environments (CASCADE) program. The spectra include (J= 1−0) transitions of HCO+, HCN, HNC, N2H+, H2CO, and CCH, which trace different temperature and density regimes of the outflowing gas at high velocity resolution (~0.8 km s−1). The map encompasses the entire DR21 Main outflow and covers all spatial scales down to a resolution of 3″ (~0.02 pc).Results.Integrated intensity maps of the HCO+emission reveal a strongly collimated bipolar outflow with significant overlap of the blueshifted and redshifted emission. The opening angles of both outflow lobes decrease with velocity, from ~80 to 20° for the velocity range from 5 to 45 km s−1relative to the source velocity. No evidence is found for the presence of elongated, “filament-like” structures expected in explosive outflows. N2H+emission near the western outflow lobe reveals the presence of a dense molecular structure, which appears to be interacting with the DR21 Main outflow.Conclusions.The overall morphology as well as the detailed kinematics of the DR21 Main outflow are more consistent with a typical bipolar outflow than with an explosive counterpart.

Development of novel cytoprotective small compounds inhibiting mitochondria-dependent cell death

We identified cytoprotective small molecules (CSMs) by a cell-based high-throughput screening of Bax inhibitors. Through a medicinal chemistry program, M109S was developed, which is orally bioactive and penetrates the blood-brain/retina barriers. M109S protected retinal cells in ocular disease mouse models. M109S directly interacted with Bax and inhibited the conformational change and mitochondrial translocation of Bax. M109S inhibited ABT-737-induced apoptosis both in Bax-only and Bak-only mouse embryonic fibroblasts. M109S also inhibited apoptosis induced by staurosporine, etoposide, and obatoclax. M109S decreased maximal mitochondrial oxygen consumption rate and reactive oxygen species production, whereas it increased glycolysis. These effects on cellular metabolism may contribute to the cytoprotective activity of M109S. M109S is a novel small molecule protecting cells from mitochondria-dependent apoptosis both invitro and invivo. M109S has the potential to become a research tool for studying cell death mechanisms and to develop therapeutics targeting mitochondria-dependent cell death pathway.

Don’t we know enough about models? Integrating a replication study into an introductory chemistry course in higher education

Abstract This paper presents the German translation and replication of the Students’ Understanding of Models in Science (SUMS) instrument, aiming to assess how first-semester university students comprehend the submicroscopic level in chemistry courses. The assessment of students’ understanding is a prerequisite for improving teaching practices, particularly in addressing the persistently high drop-out rates observed in chemistry and chemistry-related programs. Employing a quantitative methodology, a sample of 181 undergraduate chemistry students was surveyed. The data were analyzed using structural equation modeling, resulting in two statistical models that demonstrated an excellent fit to the data, although no empirical preference could be established for one model over the other. Based on the investigation, framing models as exact replicas of the natural world cannot be considered an empirically meaningful dimension of understanding models in science. Additionally, the reliabilities of the latent constructs were found to be insufficiently low to establish generalizable measurements. These findings are discussed with a focus on epistemology and advocate for a stronger integration of model theory in chemistry teaching and learning. Finally, the importance of establishing a stronger connection between empirical evidence and the implementation of curricular changes in higher education is emphasized.

PubChemQC B3LYP/6-31G*//PM6 Data Set: The Electronic Structures of 86 Million Molecules Using B3LYP/6-31G* Calculations.

The presented "PubChemQC B3LYP/6-31G*//PM6" data set is composed of the electronic properties of 85,938,443 molecules, encompassing a broad spectrum of molecules from essential compounds to biomolecules with a molecular weight up to 1000. These molecules account for 94.0% of the original PubChem Compound catalog as of August 29, 2016. The electronic properties, including orbitals, orbital energies, total energies, dipole moments, and other pertinent properties, were computed by using the B3LYP/6-31G* and PM6 methods. The data set, available in three formats, namely, GAMESS quantum chemistry program files, selected JSON output files, and a PostgreSQL database, provides researchers with the ability to query molecular properties. It is further subdivided into five subdata sets for more specific data. The first two subsets encompass molecules with carbon, hydrogen, oxygen, and nitrogen with molecular weights under 300 and 500, respectively. The third and fourth subsets incorporate molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, and chlorine, with molecular weights under 300 and 500, respectively. The fifth subset comprises molecules with carbon, hydrogen, nitrogen, oxygen, phosphorus, sulfur, fluorine, chlorine, sodium, potassium, magnesium, and calcium, with a molecular weight of under 500. The coefficients of determination for the highest occupied molecular orbital-lowest unoccupied molecular orbital energy gap range from 0.892 (for CHON500) to 0.803 (for the whole data set). These comprehensive results pave the way for applications in drug discovery and materials science, among others. The data sets can be accessed under the Creative Commons Attribution 4.0 International license at the following web address: https://nakatamaho.riken.jp/pubchemqc.riken.jp/b3lyp_pm6_datasets.html.

Investigation the Association Between the Moroccan High School Chemistry Program and Students’ Reasoning

In this study, we discuss the achievements of learners and their abilities to mobilize these achievements in solving complex and everyday life situations. For this purpose, we analyzed the institutional guidelines for the teaching of chemistry, in particular redox transformations and some related concepts. Then, we developed a test targeting two categories of items: the first category covers epistemic aspects while the second goes over the mobilization of redox knowledge in everyday life. This test was administered to a sample of high school students. The results of this empirical research indicate that these learners only master some fragmented knowledge and therefore they cannot mobilize it in an integrated way in everyday situations. The analysis shows that the majority of the difficulties identified can be interpreted by a poor appropriation of chemical concepts related to the microscopic structure of the substance. The difficulties encountered could stem from the structure of the program recommended in the curriculum and the methods of assessing learning. Indeed, our content analysis reveals that the didactic transposition operated for the conceptualization of the concepts related to chemical transformations rather favors the memorization of declarative and procedural knowledge. Moreover, the construction of knowledge follows a linear and not a spiral progression. The current research explores these empirical findings and proposes implications for content progression, learning and assessment of learning.
 
 Received: 24 June 2023 / Accepted: 09 August 2023 / Published: 5 September 2023

Bisindolyl Maleimides and Indolylmaleimide Derivatives-A Review of Their Synthesis and Bioactivity.

The evolution of bisindolyl maleimides and indolyl maleimide derivatives and their unique biological activities have stimulated great interest in medicinal chemistry programs. Bisindolylmaleimide (BIM)-type compounds arise from natural sources such as arcyriarubin and are biosynthetically related to indolocarbazoles. BIMs are commonly the immediate synthetic precursors of indolocarbazoles, lacking a central bond between the two aromatic units and making them more flexible and drug-like. Synthetic endeavours within this class of compounds are broad and have led to the development of both remarkably potent and selective protein kinase inhibitors. Clinical BIM examples include ruboxistaurin and enzastaurin, which are highly active inhibitors of protein kinase C-β. While BIMs are widely recognised as protein kinase inhibitors, other modes of activity have been reported, including the inhibition of calcium signalling and antimicrobial activity. Critically, structural differences can be used to exploit new bioactivity and therefore it is imperative to discover new chemical entities to address new targets. BIMs can be highly functionalised or chemically manipulated, which provides the opportunity to generate new derivatives with unique biological profiles. This review will collate new synthetic approaches to BIM-type compounds and their associated bioactivities with a focus on clinical applications.

Molecular dynamic study of abrasive wear, viscosity and moduli of UDMA: A component of dental composite

Among the dental composites, Urethane Dimethacrylate (UDMA) is commonly used as a component in treating oral complications. Many molecular dynamics approaches are used to understand the behaviour of the material at room temperature as well as at higher temperatures to get a better insight after comparison with experimental values at the atomic level. There are three critical physical properties associated with these components, like abrasive wear, viscosity, and moduli, which play an essential role in determining the treatment and can be computed using the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), the general-purpose quantum chemistry program package (ORCA), and the General Utility Lattice Program (GULP) molecular dynamics methods. A radial distribution function plot is generated using visual molecular dynamics (VMD) for UDMA and BisGMA. A comparison of these parameters with BisGMA, another component of dental composites, along with experimental results, is carried out in the present investigation. Further, since radiation also matters for settling the materials in dental treatment, we have computed absorption spectra from 200 nm to 800 nm using LAMMPS/ORCA.

Simple, Efficient, and Universal Energy Decomposition Analysis Method Based on Dispersion-Corrected Density Functional Theory.

Energy decomposition analysis (EDA) is an important class of methods to explore the nature of interaction between fragments in a chemical system. It can decompose the interaction energy into different physical components to understand the factors that play key roles in the interaction. This work proposes an EDA strategy based on dispersion-corrected density functional theory (DFT), called sobEDA. This method is fairly easy to implement and very universal. It can be used to study weak interactions, chemical bond interactions, open-shell systems, and interactions between multiple fragments. The total time consumption of sobEDA is only about twice that of conventional DFT single-point calculation for the entire system. This work also proposes a variant of the sobEDA method named sobEDAw, which is designed specifically for decomposing weak interaction energies. Through a proper combination of DFT correlation energy and dispersion correction term, sobEDAw gives a ratio between dispersion energy and electrostatic energy that is highly consistent with the symmetry-adapted perturbation theory, which is quite popular and robust in studying weak interactions but expensive. We present a shell script sobEDA.sh to implement the methods proposed in this work based on the very popular Gaussian quantum chemistry program and Multiwfn wavefunction analysis code. Via the script, theoretical chemists can use the sobEDA and sobEDAw methods very conveniently in their study. Through a series of examples, the rationality of the new methods and their implementation are verified, and their great practical values in the study of various chemical systems are demonstrated.

The Importance of the "Time Factor" for the Evaluation of Inhibition Mechanisms: The Case of Selected HDAC6 Inhibitors.

Histone deacetylases (HDACs) participate with histone acetyltransferases in the modulation of the biological activity of a broad array of proteins, besides histones. Histone deacetylase 6 is unique among HDAC as it contains two catalytic domains, an N-terminal microtubule binding region and a C-terminal ubiquitin binding domain. Most of its known biological roles are related to its protein lysine deacetylase activity in the cytoplasm. The design of specific inhibitors is the focus of a large number of medicinal chemistry programs in the academy and industry because lowering HDAC6 activity has been demonstrated to be beneficial for the treatment of several diseases, including cancer, and neurological and immunological disorders. Here, we show how re-evaluation of the mechanism of action of selected HDAC6 inhibitors, by monitoring the time-dependence of the onset and relief of the inhibition, revealed instances of slow-binding/slow-release inhibition. The same approach, in conjunction with X-ray crystallography, in silico modeling and mass spectrometry, helped to propose a model of inhibition of HDAC6 by a novel difluoromethyloxadiazole-based compound that was found to be a slow-binding substrate analog of HDAC6, giving rise to a tightly bound, long-lived inhibitory derivative.

BasisOpt: A Python package for quantum chemistry basis set optimization.

The accuracy and efficiency of molecular quantum chemical calculations depend critically on the basis set used. However, the development of novel basis sets is hindered because much of the literature relies on the use of opaque processes and tools that are not publicly available. We present here BasisOpt, a tool for the automated optimization of basis sets with an easy-to-use framework. It features an open and accessible workflow for basis set optimization that can be easily adapted to almost any quantum chemistry program, a standardized approach to testing basis sets, and visualization of both the optimized basis sets and the optimization process. We provide examples of usage in realistic basis set optimization scenarios where: (i) a density fitting basis set is optimized for He, Ne, and Ar; (ii) the exponents of the def2-SVP basis are re-optimized for a set of molecules rather than atoms; and (iii) a large, almost saturated basis of sp primitives is automatically reduced to (10s5p) while achieving the lowest energy for such a basis set composition.

Identification of a Non-Nucleoside Reverse Transcriptase Inhibitor against Human Immunodeficiency Virus-1.

The HIV-1 infection epidemic remains a global health problem. Current antiretroviral treatments are effective in controlling the progression of a severe infection. However, the emergence of drug resistance requires an urgent identification of new treatment regimes. HIV-1 reverse transcriptase (RTs) has been a successful therapeutic target owing to its high specificity and potent antiviral properties; therefore, it has become an essential component of current HIV-1 standard treatments. This study identified a new HIV-1 RTs inhibitor (Compound #8) that is structurally unique and greatly effective against HIV-1 through chemical library screening and a medicinal chemistry program by analyzing the structure-activity relationship (SAR). Further analysis of molecular docking and mechanisms of action demonstrated that Compound #8 is a novel type of HIV-1 non-nucleoside reverse transcriptase inhibitor (NNRTI) with a flexible binding mode. Therefore, it exhibits great therapeutic potential when combined with other existing HIV-1 drugs. Our current studies suggest that Compound #8 is a promising novel scaffold for the development of new HIV-1 treatments.

Entropy structural characterization of zeolites BCT and DFT with bond-wise scaled comparison

Entropy of a connected network is a quantitative measure from information theory that has triggered a plethora of research domains in molecular chemistry, biological sciences and computer programming due to its inherent capacity to explore the structural characteristics of complex molecular frameworks that have low structural symmetry as well as high diversity. The analysis of the structural order is greatly simplified through the topological indices based graph entropy metrics, which are then utilized to predict the structural features of molecular frameworks. This predictability has not only revolutionized the study of zeolitic frameworks but has also given rise to new generations of frameworks. We make a comparative study of two versatile framework topologies namely zeolites BCT and DFT, which have been widely utilized to create a new generation of frameworks known as metal organic frameworks. We discuss bond-additive topological indices and compute entropy measure descriptors for zeolites BCT and DFT using degree and degree-sum parameters. In addition, we perform bond-wise scaled comparative analysis between BCT and DFT which shows that zeolite BCT has greater entropy values compared to zeolite DFT.