The Mambaka watershed is extends between latitudes 1 3°45'E and 14°15'E and longitudes 7°16'N and 6°45'N. The geology, various tectonic and structural events that have affected the Adamawa Plateau in Cameroon make it rich in multi-substance mining. The objective of this study is to map rare earth (REE) geochemical anomalies in the sediments of the watershed streams west of Mambaka, and to trace their origins and geochemical processes. Predictive maps from inverse distance interpolations (IDW), factor analysis (F1) or principal component analysis (PCA) and hierarchical bottom-up classification maps provided a better understanding of the central tendency, distribution and dispersion of REE in the samples and in the study area, based on standard deviation and variance values that generated two factors F1 (Ho-Tm-Er-Yb-Lu-Dy-Tb-Gd-Eu-Sm) and F2 (Pr-Nd-Ce-La-Sm) representing 92.44% of the total cumulative variance. The ratios Ce/Ce* > 0.78 and Eu/Eu* > 1 demonstrate positive anomalies in Ce and Eu, and clear differentiation. The normalized concentrations used to calculate fractionation ratios show that the values for LaN/YbN (0.58 to 1.34), LaN/SmN (0.61 to 0.88) and LaN/LuN (0.62 to 1.43) suggest higher fractionation in SS09 and lower fractionation in SS01. Similarly, the ratios La/Lu (61.71 to 143.46), La/Yb (9.00 to 20.72), La/Sm (4.02 to 5.83) and La/ Lu (61.71 to 143.46) confirm these higher ratios in SS09 and lower in SS01. The REE in the study area comes from hydrothermal processes based on high lineament densities at sampling points in igneous rocks with a mean ∑REE value of between 174-219 ppm.

During the past few decades, many of the synthetic chemicals are able to produce nanoparticles and nanoclusters, although these chemicals primarily act as reducing and capping agents, they are very toxic and hazardous and make the nanoparticles biologically incompatible. Thus there is need for green chemistry that includes a clean, non-toxic and environmental friendly method of nanoparticles synthesis. Cobalt, iron and copper nanoparticles were synthesized using the stem-bark extract of khayasenegalensis (mahogany) where cobalt chloride (CoCl₂ 6H₂O), ferric chloride (FeCl₂), and copper sulphate (CuSO₄ H₂O) were used as the metal precursor respectively. The change in color from light brown to dark brown indicates the formation of cobalt nanoparticles, from light brown to dark green indicates the formation of copper nanoparticles and also the change in color from light brown to a dark color indicates formation of iron nanoparticles. The nanoparticles were further characterized using UV visible spectroscopy, FTIR, and SEM. The UV result for CoNPs showed the highest peak at 500nm and both FeNPs and CuNPs showed the highest peak at 300nm. The FTIR results for all the nanoparticles showed the presence of Alkaloids and triterpenes. Also the SEM result showed spherical granular, partially dispersed and monodispersed morphology for CoNPs, FeNPs and CuNPs respectively. Moreover, the antibacterial activity of the synthesized NPs when tested against two gram positive bacteria and two gram negative bacteria was evaluated and good results were obtained. The antifungal activity when tested against two fungi showed a very good result.

Three novel coordination polymers {[Cu₂(bitbu-OMe)₄(SO₄)₂]·6MeOH}_n (1), {[Co₂(bitbu-OMe)₄(NCS)₄]_0.5·2DMF}_n (2), {[Co(bitbu-OMe)₂(NCS)₂]·2MeOH}_n (3) (bitbu-OMe = 1,1’-[(5-tert-butyl-2-methoxybenzene-1,3-diyl)dimethanediyl]bis(1H-imidazole)) are synthesized through a slow evaporation method using solvothermal technique of CuSO₄·5H₂O or Co(SCN)₂ with bitbu-OMe. X-ray diffraction analysis results reveal that 1, 2, and 3 have similar two-dimensional layer networks. The study of the effect of the methoxy group in bitbu-OMe towards the stability of ligand conformation in obtained coordination polymers becomes necessary to be conducted in the future to unveil the reason for conformation similarity of ligand in coordination polymers.

Polychlorinated biphenyls (PCBs) are important class of persist organic pollutants that were used as a component of paints especially in printings, as plastificator of plastics and insulating materials in transformers and capacitors, heat transfer fluids, additives in hydraulic fluids in vacuum and turbine pumps. There is always a need to establish reliable procedures for predicting the bioconcentration potential of chemicals from the knowledge of their molecular structure, or from readily measurable properties of the substance. Hence, correlation and prediction of biococentration factors (BCFs) based on λ_max and vibration frequencies of various bonds viz υ(C-H) and υ(C=C) of biphenyl and its fifty-seven derivatives have been made. For the study, the molecular modeling and geometry optimization of the PCBs have been performed on workspace program of CAChe Pro 5.04 software of Fujitsu using DFT method. UV-visible spectra for each compound were created by electron transition between molecular orbitals as electromagnetic radiation in the visible and ultraviolet (UV-visible) region is absorbed by the molecule. The energies of excited electronic states were computed quantum mechanically. IR spectra of transitions for each compound were created by coordinated motions of the atoms as electromagnetic radiation in the infrared region is absorbed by the molecule. The force necessary to distort the molecule was computed quantum mechanically from its equilibrium geometry and thus frequency of vibrational transitions was predicted. Project Leader Program associated with CAChe has been used for multiple linear regression (MLR) analysis using above spectroscopic data as independent variables and BCFs of PCBs as dependent variables. The reliability of correlation and predicting ability of the MLR equations (models) are judged by R², R²_adj, se, q²_L10O and F values. This study reflected clearly that UV and IR spectroscopic data can be used to predict BCFs of a large number of related compounds within limited time without any difficulty.

Sentiment analysis, a vital aspect of natural language processing, involves the application of machine learning models to discern the emotional tone conveyed in textual data. The use case for this type of problem is where businesses can make informed decisions based on customer feedback, identify the sentiments of their employees, and make decisions on hiring or retention, or for that matter, classify a text based on its topic like whether it is about a particular subject like physics or chemistry as is useful in search engines. The model leverages a sequential architecture, transforms words into dense vectors using an Embedding layer, and captures intricate sequential patterns with two Long Short-Term Memory (LSTM) layers. This model aims to effectively classify sentiments in text data using a 50-dimensional embedding dimension and 20 % dropout layers. The use of rectified linear unit (ReLU) activations enhances non-linearity, while the SoftMax activation in the output layer aligns with the multi-class nature of sentiment analysis. Both training and test accuracy were well over 80%.

The synthesis and optimization of Active Pharmaceutical Ingredients (APIs) is fundamental to pharmaceutical drug development, directly influencing drug efficacy, safety, and cost-effectiveness. Over recent years, significant advancements in synthetic methodologies and manufacturing technologies have transformed API production. This manuscript provides an overview of the latest innovations in API synthesis, focusing on key techniques such as green chemistry, continuous flow chemistry, biocatalysis, and automation. Green chemistry principles, including solvent substitution and catalytic reactions, have enhanced sustainability by reducing waste and energy consumption. Continuous flow chemistry offers improved reaction control, scalability, and safety, while biocatalysis provides an eco-friendly alternative for synthesizing complex and chiral APIs. Additionally, the integration of automation and advanced process control using machine learning and real-time monitoring has optimized production efficiency and consistency. The manuscript also discusses the challenges associated with regulatory compliance and quality assurance, highlighting the role of advanced analytical techniques such as HPLC, NMR, and mass spectrometry in ensuring API purity. Looking ahead, personalized medicine and smart manufacturing technologies, including blockchain for traceability, are expected to drive further innovation in API production. This review concludes by emphasizing the need for continued advancements in sustainability, efficiency, and scalability to meet the evolving demands of the pharmaceutical industry, ultimately enabling the development of safer, more effective, and environmentally responsible medicines.

Active Pharmaceutical Ingredients (APIs) serve as the cornerstone of pharmaceutical development, driving therapeutic efficacy and safety in drug formulations. This article provides a comprehensive overview of the lifecycle of APIs, starting from their discovery and development, through to manufacturing processes and regulatory oversight. The development of APIs begins with intensive research and discovery efforts, where medicinal chemists and pharmacologists identify and optimize potential compounds through computational modelling, high-throughput screening, and structure-activity relationship studies. Promising candidates undergo rigorous preclinical testing to assess pharmacological properties, safety profiles, and potential adverse effects in animal models. Upon successful preclinical outcomes, APIs progress to clinical trials, involving phases of testing in human subjects to evaluate efficacy, dosage regimens, and safety profiles under controlled conditions. Clinical trial data are meticulously analyzed to support regulatory submissions, demonstrating the API's therapeutic benefits and safety for eventual patient use. Manufacturing APIs involves complex chemical synthesis or biotechnological methods, ensuring precise control over reaction conditions, purity, and yield. The scale-up from laboratory synthesis to industrial production demands adherence to Good Manufacturing Practices (GMP), where stringent quality control measures verify consistency, potency, and stability throughout production batches. Regulatory oversight by authorities such as the Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) in Europe ensures that APIs meet stringent standards of safety, efficacy, and quality before market approval. Manufacturers must submit comprehensive Chemistry, Manufacturing, and Controls (CMC) data, detailing manufacturing processes, analytical methods, and stability studies to support regulatory filings.

Active Pharmaceutical Ingredients (APIs) form the backbone of pharmaceutical formulations, influencing their efficacy, safety, and stability. Essence control of APIs involves stringent regulation and optimization of their chemical, physical, and biological properties to ensure consistent quality and therapeutic outcomes. This manuscript explores the critical aspects of essence control in APIs, including synthesis, characterization, quality assessment, and regulatory considerations. The synthesis of Active Pharmaceutical Ingredients is a pivotal stage in pharmaceutical manufacturing, where precise control over chemical reactions and process conditions is paramount to achieving high-quality, safe, and effective medicines. Advances in synthetic methodologies, optimization strategies, sustainability practices, and the implementation of PAT technologies continue to drive innovation in API synthesis, supporting the development of novel therapeutic agents and enhancing pharmaceutical manufacturing efficiency.

The purpose of this study was to use an activity-based method to enhance the teaching and learning of Redox reactions among senior high school learners at Christ the King at Obuasi in the Ashanti Region, Ghana. Quantitatively, the study employed an action research design. The population of the study comprised all final-year elective chemistry students of Christ the King Senior High School (CKC) in the Ashanti region of Ghana. A purposive sampling technique was used to select thirty-five (35). The instruments used in the study were tested. Percentages of students who responded correctly to the pre-test items were compared to percentages of students who responded correctly to the post-test items. The pre-test and post-test mean scores were compared to see if there was any difference in their mean scores. The use of an activity-based teaching method in teaching chemistry appears to be used effectively in imparting the content knowledge of chemistry to students to become successful in their learning. Regarding the benefits of the activity-based method. The use of activity-based teaching methods in redox reaction motivates students to be self-learners and improves performance. It is also evident from the findings of this study that the use of the activity-based method of teaching could enhance student performance in a redox reaction. It is recommended that activity-based methods of teaching should be encouraged to be used by chemistry teachers in the Senior High Schools of Ghana in teaching redox reaction concepts to enhance students’ performance in redox reactions. It is also recommended that the Ghana education service should collaborate with the chemistry teachers’ Association of Ghana to organize professional development programs, seminars, and workshops for chemistry teachers on activity-based to improve their knowledge of teaching skills.