The University of Cartagena (Spanish: Universidad de Cartagena), also called Unicartagena, is a public, departmental, coeducational, research university based primarily in the city of Cartagena, Bolívar, Colombia. The university offers education at undergraduate and postgraduate levels, which includes two doctorates.The university is member of the Association of Colombian Universities (ASCUN), the Iberoamerican Association of Postgraduate Universities (AUIP), and the State University System (Spanish: Sistema Universitario Estatal, SUE).On March 26, 2014, the University of Cartagena, received from the Ministry of Education Institutional Accreditation of High Quality becoming the first and only public university in the Caribbean region with this type of accreditation..
The hydroformylation of alkenylbenzenes remains insufficiently defined, despite the relevance of these substrates as biomass-derived aromatic feedstocks within sustainable chemical transformations. In this work, we present an experimental (catalytic and kinetic) study of their conversion into aldehydes under rhodium-phosphine catalysis, using complexes bearing mono-, bi- and tridentate phosphine ligands, [Rh(H)(CO)(2)(PPh3)(2)], [Rh(H)(CO)(triphos)] and [Rh(H)(CO)(2)(dppe)], under mild reaction conditions (80 degrees C and 2-30 bar of syngas for eugenol; 80 degrees C and 20-50 bar of syngas for estragole and trans-anethole). The catalytic activity order of the complexes was Rh (PPh3) > Rh(triphos) > Rh(dppe), while the substrate reactivity followed the trend eugenol > estragole >> trans-anethole. Reaction rates were measured across a wide CO and H-2 pressure range, revealing redistribution between the active monocarbonyl species and an off-cycle (acyl)dicarbonyl complex that becomes dominant at elevated p(CO). The kinetic behavior observed for eugenol hydroformylation with Rh(PPh3) was consistent with the established hydroformylation sequence involving alkene coordination, hydride migration to substrate and the CO-dependent re-coordination steps that determine catalyst speciation; the subsequent transfer of the alkyl group to the carbonyl ligand and hydrogenolysis complete the catalytic cycle; the H-2 addition or the hydride transfer to the alkene was identified as the rate-determining step, depending on whether low or high p(H-2) values were employed. To obtain statistically reliable kinetic parameters- often challenging in hydroformylation because of parameter covariance and restricted identifiability- we complemented conventional nonlinear regression with Bayesian inference based on the Markov Chain Monte Carlo approach. The resulting posterior distributions were well centered, exhibited realistic variance and provided parameter sets that are sufficiently robust to support mechanistic interpretation and subsequent kinetic modeling.
This study evaluates the glycolysis of post-consumer polyethylene terephthalate (PET) using NiZnAl-derived catalysts obtained from hydrotalcite precursors, with a particular focus on the influence of operational variables and catalyst properties. A statistical approach based on response surface methodology (RSM) was applied to analyze the effects of reaction temperature, propylene glycol volume, catalyst mass, and catalyst calcination temperature on PET degradation. The results indicate that reaction temperature is the most influential variable, showing a significant positive effect on PET conversion, while propylene glycol volume and catalyst mass exhibit moderate but statistically relevant effects. In contrast, catalyst calcination temperature was not found to be statistically significant within the studied experimental domain. The fitted quadratic model suggests that the response is primarily governed by linear effects, with limited contribution from interaction and quadratic terms, indicating a system dominated by first-order trends. Experimental observations further suggest that increasing solvent volume and catalyst loading may introduce mass transfer limitations and dilution effects, reducing the efficiency of the depolymerization process. Catalysts calcined at intermediate temperatures showed higher average degradation, which may be related to differences in surface structure and accessibility of active sites, although this effect cannot be considered dominant. The predictive capability of the model was validated through scale-up experiments, where degradation values remained consistent with model predictions, demonstrating the applicability of RSM for process optimization. Overall, PET glycolysis in this system appears to be mainly controlled by thermal activation, while catalyst properties contribute to the efficiency of the reaction without acting as the primary governing factor. PET Glycolysis with Ni–Zn–Al Catalysts: Optimization via RSM.
Density functional theory (DFT) was used to investigate how bisubstitution doping in graphene alters its electronic structure and interfacial stability with two model lignocellulosic binders, carboxymethylcellulose (CMC), and a representative aromatic fragment (LCmA). The properties were evaluated at the omega B97X-D/LANL2DZ level for pristine graphene and its bisubstitution-doped variants with nitrogen (graphene-2N) and sulfur (graphene-2S), integrating frontier orbitals, electrostatic potential (ESP) maps, electronic localization functions (ELF/LOL), and QTAIM topology. Doping with 2N markedly reduces the HOMO-LUMO gap from 0.16052 eV (graphene) to 0.10560 eV (-34.2%), while 2S reduces it to 0.14222 eV (-11.4%), evidencing different electronic activation mechanisms. The interaction energies show doping-controlled selectivity: In pristine graphene, adsorption strongly favors LCmA (Delta Eint = -99.3 kcal & centerdot;mol(-1)) over CMC (-23.7 kcal & centerdot;mol(-1)); in graphene-2N, CMC coupling intensifies (-93.7 kcal & centerdot;mol(-1)) while maintaining a high interaction with LCmA (-74.3 kcal & centerdot;mol(-1)); and in graphene-2S, CMC remains favorable (-71.9 kcal & centerdot;mol(-1)) while LCmA falls to a practically marginal regime (-4.1 kcal & centerdot;mol(-1)). QTAIM the presence of confirms closed-layer interactions in all complexes (del Pc-2 > 0, H > 0, |V|/G < 1), with |V|/G close to unity for graphene-LCmA (0.994) and less compaction when doped with 2N (0.760 for 2N-LCmA). The bisubstitution modulates the electronic heterogeneity of the basal plane and redefines the binder-surface compatibility, favoring the multipoint anchoring of polar ligands in 2N and penalizing efficient aromatic stacking in 2S.
Considering the challenge in hyperspectral imaging of developing new computational methods that strike a balance between accurate material classification and computational complexity, this work proposes the design and tunability of a model based on a sequential artificial neural network (ANN) to classify vegetation in hyperspectral images with 380 bands. To carry out this research, an adaptation of the CRISP-DM methodology was used, structured into four phases: P1. Business and data understanding, P2. Data preparation, P3. Modeling and evaluation, and P4. Modl application. As a result, a sequential ANN model was developed, featuring 380 input layers and a single output layer, along with a set of dense layers containing 12, 8 and 4 artificial neurons. After 20 epochs, the model showed high performance and consistent behavior in the training and test sets under the experimental setup considered. The model was applied to a hyperspectral image of the Manga neighborhood in Cartagena, classifying 41.921% of the image pixels as vegetation. This percentage of points exceeds by 12.941% the percentage obtained by the spectral differential similarity method, in which less continuous point detections were observed. This method is a viable alternative for use in environmental monitoring systems, especially when applied in parallel
Converting carbon dioxide (CO2) into value-added chemicals and/or capturing it before emission are complementary strategies to mitigate rising atmospheric CO2 levels. Copper-based materials are widely investigated for CO2 conversion because Cu can bind and electronically activate CO2 and related intermediates. In this computational research, an evaluation of CO2 activation in CuxSc gamma nanoclusters (Cu3Sc, Cu2Sc2, and CuSc3) anchored on a graphene bilayer doped with three nitrogen atoms (graphene-3N) was performed using conformational screening and thermochemical adsorption analysis at 298.15, 300, and 400 K. Initially, the Cu3Sc, Cu2Sc2, and CuSc3 nanoclusters were optimized and characterized (relative energy, multiplicity, and electronic characteristics), and the support model (graphene-3N bilayer) was validated by comparing free geometry with partially restricted geometry, corroborating minima through vibrational analysis. Subsequently, CO2 adsorption/activation on CuxSc gamma @graphene-3N was evaluated, and Delta H and Delta G values were calculated. Ultimately, based on the Delta G(T) values, the Sabatier regimes were established, where it was observed that Cu3Sc exhibits moderate exergonic adsorption (Delta G = -76.07, -67.31, and -58.92 kJ & centerdot;mol(-1) at 298.15, 350, and 400 K). In contrast, Cu2Sc2 exhibits intense adsorption (-165.02, -156.36, and -148.04 kJ & centerdot;mol(-1)), and CuSc3 results in practically irreversible fixation (-293.98, -287.32, and -279.09 kJ & centerdot;mol(-1)), giving priority to Cu3Sc as the most optimal cluster in terms of activation-regeneration.
