Global Impact Journal: Chemistry & Applications

Application of Machine Learning Models for Predicting pIC50 Values of Plasticizers Against Cytochrome P450 Aromatase

Thu, 27 Aug 2026 00:00:00 +0000

This study investigated the application of six machine learning regression algorithms such as Random Forest, CatBoost, K-Nearest Neighbours, XGBoost, LightGBM, and Gradient Boosting, paired with Molecular ACCess System (MACCS) key fingerprints for the quantitative prediction of aromatase (CYP19A1) inhibitory potency, expressed as pIC₅₀. A dataset of 187 compounds was assembled from the ChEMBL database (version 33, Target ID: CHEMBL1978) following by systematic data curation workflow encompassing duplicate removal, pIC₅₀ transformation, and activity-based filtering. Model performance was rigorously evaluated using an 80/20 stratified train/test split, 5-fold cross-validation, and Y-randomisation testing to ensure unbiased assessment of predictive generalisation. Feature selection via CatBoost permutation importance on the held-out test set identified the top 20 predictive MACCS keys from an initial 166-bit space, substantially reducing dimensionality and improving generalisation across all models. Among the algorithms evaluated, CatBoost trained on the top 20 features achieved the strongest test-set performance (R² = 0.693, RMSE = 0.794, MAE = 0.659) with the most stable cross-validation R² (0.062 ± 0.304), outperforming all other algorithms. Y-randomisation testing returned an empirical p-value of <0.01, confirming that model performance reflects genuine structure–activity relationships rather than statistical chance. Permutation importance and SHAP analysis identified nitrogen-containing heterocyclic fragments (MACCS_41, MACCS_145) and halide-bearing substructures (MACCS_109) as the primary structural determinants of aromatase inhibitory potency, consistent with established CYP19A1 pharmacophoric requirements. Application of the model to ten representative plasticizers demonstrated that the refined applicability domain (h^* = 0.423) accommodated eight of the ten compounds, enabling reliable potency predictions across phthalate esters and bisphenol analogues. These findings establish a transparent and reproducible QSAR framework for first-tier endocrine disruption risk screening of plasticizers and highlight the importance of permutation-based feature selection and applicability domain assessment in QSAR model development.

Chromatographic Method for Simultaneous Quantification of Gemcitabine and Olaparib Recovered from Isolated Pancreatic Tissue

Thu, 27 Aug 2026 00:00:00 +0000

The combination of gemcitabine (GEM) and olaparib (OLA) shows promise for treating pancreatic cancer, particularly in patients with mutations in the BRCA genes. This work presents the validation of a straightforward, fast, and sensitive chromatographic method for the simultaneous analysis of GEM and OLA, supporting the development of advanced pharmaceutical formulations that combine the two drugs. The efficient chromatographic separation of GEM and OLA was achieved using a C18 column (250 × 4.6 mm, 5 μm) with a mobile phase composed of acetonitrile and water (50:50, v/v), which eluted isocratically at a flow rate of 0.8 mL/min. Determinations were performed using a PDA detector at 243 nm for both drugs. The retention times for GEM and OLA were approximately 3.3 and 4.3 min, respectively. The method was linear (R² > 0.999), with a regression curve in the concentration range of 0.5 to 10.0 μg/mL, demonstrating sensitivity, precision, and accuracy. The recovery rates of the drugs from the pancreatic tissue were higher than 97.0%. The components of a coated liposomal formulation and the pancreatic tissue did not interfere with the analysis, and both drugs demonstrated a low degradation rate under stressful conditions. In conclusion, the validated method was suitable for quantifying GEM and OLA simultaneously, even in a biological matrix, making it feasible to support the development of advanced pharmaceutical formulations that incorporate both drugs, such as liposomes.

Relationship Between the Morphology and Catalytic Properties of Mn-Ni Multiphase Nanostructures for the Reduction of 4-Nitrophenol

Philip Asare, J. Jesús Velázquez Salazar, Miguel José Yacamán — Thu, 27 Aug 2026 00:00:00 +0000

The catalytic reduction of 4-nitrophenol (4-NP) requires highly efficient and cost-effective materials, making Mn-Ni nanostructures a promising candidate. In this study, Mn-Ni nanoparticles with distinct morphologies (specifically spheres, stars, and core–shell structures) were synthesized by tuning the precursor composition. The structural and optical properties of the synthesized catalysts were characterized via electron microscopy and UV-Vis spectroscopy. Kinetic evaluations of the 4-NP reduction demonstrated that the core–shell architecture yielded the highest catalytic activity, outperforming both the star-shaped and spherical nanoparticles. Furthermore, the high-index facets at the tips of the nanostars appeared to have contributed to enhanced catalytic activity by providing additional active surface sites for the reduction process. Ultimately, this work suggests that structural morphology and interfacial interactions play important roles in determining catalytic performance, rather than absolute surface area, providing valuable insights into the design of future bimetallic catalysts.

Metal-Assisted Exfoliation of Two-Dimensional Materials: From Mechanisms to Large-Scale Applications

Manyao Wang, Zongyu Huang, Yang Chen, Xiang Qi — Thu, 27 Aug 2026 00:00:00 +0000

In the post-Moore era, large-area manufacturing of high-quality two-dimensional (2D) materials remains a central bottleneck for the industrialization of next-generation microelectronic and optoelectronic devices. Conventional mechanical exfoliation is limited by randomness and small lateral size, whereas chemical vapor deposition inevitably introduces grain boundaries, stress, and interfacial contamination, making it difficult to achieve both high quality and scalability. Metal-assisted exfoliation (MAE) enables controllable exfoliation and nondestructive transfer of large-area, high-quality monolayer 2D materials via precise modulation of metal-2D interfacial interactions dominated by strain-induced decoupling and atomic intercalation. This article systematically outlines the interfacial physical mechanisms and technological evolution of MAE, and highlights its state-of-the-art applications in patterned transfer, high-performance field-effect transistors, and complementary logic circuits, aiming to provide a firm theoretical and technical basis for advancing 2D materials from fundamental research toward practical applications.

Commercial-Scale Demonstration of Carbon Capture and Utilisation (CCU) from a Nickel Refinery Off-Gas Using Microalgae in a Closed Vertical Tube Photobioreactor

Wed, 13 May 2026 00:00:00 +0000

Despite the extensive literature on microalgal production, most studies focus on controlled laboratory-scale systems, resulting in a critical lack of confidence at industrial scale. This is further compounded by the frequently observed inconsistencies, with only modest increases achieved in operational scale. This work demonstrates the design, construction, and operation of a commercial-scale tubular photobioreactor and associated equipment for the production of algae using CO₂ derived from an industrial nickel refinery. The reactor was demonstrated by growing the species Nannochloropsis gaditana. Biomass concentrations of 1.0 to 1.3 g L⁻¹ were achieved with a productivity of 0.11 g L⁻¹ d⁻¹. Extrapolation to a 300-day production year showed that the reactor was capable of producing 541.2 kg algae and sequestering around 1 tonne of CO₂. A technoeconomic assessment showed that the total plant CAPEX was £583,905 and the OPEX was £98,196. Sales of algae alone showed poor economic performance. However, economic favourability is observed for species that contain phycocyanin pigment and yield a positive net present value within 4 to 7 years based on recovery yield. This work effectively provides reliable process data developed at scale that can be used to formulate business cases for further scale-up and expansion of algal production systems. This moves the technology a step closer to full-scale realisation and the potential for a net-zero, sustainable future.