Publications

Abstract The multifaceted nature of the neurodegenerative diseases, as Alzheimer's disease (AD) and Parkinson's disease (PD) with several interconnected etiologies, and the absence of effective drugs, led herein to the development and study of a series of multi-target directed ligands (MTDLs). The developed RIV-IND hybrids, derived from the conjugation of an approved anti-AD drug, rivastigmine (RIV), with melatonin analogues, namely indole (IND) derivatives, revealed multifunctional properties, by associating the cholinesterase inhibition of the RIV drug with antioxidant activity, biometal (Cu(II), Zn(II), Fe(III)) chelation properties, inhibition of amyloid-/3 (A/3) aggregation (self- and Cu-induced) and of monoamine oxidases (MAOs), as well as neuroprotection capacity in cell models of AD and PD. In particular, two hybrids with hydroxyl-substituted indoles ( 5a2 and 5a3) ) could be promising multifunctional compounds that inspire further development of novel anti-neurodegenerative drugs.

Abstract Carbon dioxide (CO2) plays a crucial role in the biosphere, acting as an indicator of anthropogenic activity. Its monitoring is fundamental for controlling air and water quality, preserving the environment and optimizing industrial processes. The preparation of a bright fluorescent scaffold, named rhodol, was optimized by employing microwave heating as an alternative heating source, achieving shorter reaction times and higher yields. Structural characterization was performed by nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS-ESI). Its application to produce a fluorescent optical membrane for monitoring CO2 in gas (gCO2) and in water (dCO2) was explored. Two different setups are used for this purpose, and in both, the same optical response is observed: the membrane's fluorescence intensity decreases as the CO2 concentration increases. The sensor's reliability for dCO2 is demonstrated through testing concentrations ranging from 1 ppm to 100 ppm with minimal photobleaching (0.0026 dB) over 7500 data points with an integration time of 200 ms each. The sensor performance for dCO2 evaluation exhibits an experimental error of +/- 1.81 ppm, a response time of 2 min, a limit of detection of 0.6 ppm and a Stokes-shift of 90 nm for concentrations between 1 and 100 ppm. Monitoring of gCO2 using this membrane is hindered by changes in relative humidity (RH), hence the results for concentration between 0.3 % and 100 % of gCO2 were achieved by maintaining a consistent high value of RH. Our findings highlight the effectiveness of the optimized rhodol synthesis and its application in an optical membrane for reliable monitoring of CO2 in various environmental conditions.

Abstract Generative Artificial Intelligence (GAI) is rapidly transforming various sectors, including science and education. In the educational sector, GAI offers opportunities, such as personalized learning and the reduction of teacher workload, alongside challenges, like balancing the advantages of GAI with students' independent problem-solving skills, but also the potential exacerbation of the digital divide. Consequently, educational stakeholders must critically assess GAI's capabilities while remaining cognizant of its potential risks. This article aims to detail the development of a data collection instrument specifically designed to qualitatively explore the perspectives of key stakeholders regarding the integration of GAI in Science Education. The developed instrument is grounded in established theoretical frameworks, notably the Intelligent Technology, Pedagogy, and Content Knowledge model (Intelligent TPACK) and the Technology Acceptance Model (TAM), which are instrumental in understanding the multifaceted dimensions of technology integration in educational settings. The instrument aims to capture: i) indicators of respondents' literacy in GAI in science education, ii) the GAI perceived ease of use in science education and GAI tools learning curve, iii) perceived usefulness of GAI tools in enhancing science education, iv) the perceived trust in GAI tools for science education content creation and factors influencing it, v) perceptions on knowledge needed for GAI integration in science education, vi) perceptions on the acceptance of GAI tools in science education, and vii) perceptions regarding GAI ethical issues in science education. In contrast to the prevailing emphasis on the technical deployment of AI in educational contexts, this study focuses on the nuanced perspectives of science education stakeholders, to inform the development of strategies for effective GAI integration in science education and science teacher education. © 2025 IADIS Press All rights reserved.

Abstract An experimental and computational thermochemical study is reported focus on the relationship between the energetic properties, the structural characteristics and the inherent reactivity of three thiothiadiazoles: 2-amino5-(methylthio-)-1,3,4-thiadiazole, 2-amino-5-(ethylthio-)-1,3,4-thiadiazole and 2-mercapto-5-methylthio-1,3,4thiadiazole. From the DSC measurements, the enthalpies of fusion and the respective onset temperatures were determined. The standard molar energies of combustion of the (alkylthio)-1,3,4-thiadiazoles were determined by rotating bomb combustion calorimetry. The corresponding standard molar enthalpies of sublimation, at T = 298.15 K, were derived from high-temperature Calvet microcalorimetry measurements and the study of the dependence of the compound's vapour pressures with the temperature, using the Knudsen-effusion technique. The combination of these experimental results enables the calculation of the standard molar enthalpies of formation in the gaseous state, at T = 298.15 K, which are discussed in terms of structural contributions. The latter property was also determined from high-level ab initio molecular orbital calculations at the G3(MP2)//B3LYP level of theory. The computed values are in good agreement with the experimental ones. In addition, a conformational and tautomeric study was conducted using the same approach, in order to provide insight on the amino/imino and thiol/thione tautomerism of the compounds studied. The amino form predominates in aminothiadiazoles, while the thione form prevails in mercaptothiadiazole. Moreover, the relative thermodynamic stability of each compound was also evaluated showing that in both solid and gas phases, the most stable species is the mercaptothiadiazole (in its thione form). The thermochemical results of the (alkylthio)-1,3,4-thiadiazoles show a good agreement with the NBO (natural bond orbitals) analysis results.

Abstract Given the challenges associated with understanding electrochemistry content by engaging in prediction, observation, and explanation, laboratory activities can foster active participation and critical thinking, enabling individuals to proactively confront and revise their understanding. Since pre-service teachers should gain firsthand experience with predict-observe-based on a predict-observe-explain strategy, we propose a laboratory activity presented as a mystery box related to concentration cells coupled with an Arduino-based electronic data measurement system to identify how pre-service chemistry teachers move from observations to inferences in a qualitative research. Data was collected by written records and oral explanations during the practical interactions. Results suggest that the proposed laboratory activity allows pre-service chemistry teachers to access data and correct inferences related to electrochemistry content, promoting critical thinking. Data also showed a connection between the macroscopic and symbolic domains, and pre-service teachers recognized concepts associated with the activity, such as solution conductivity and the potential difference in chemical reactions, interpreting the system with prior knowledge, like electron flow direction and concentration cell components. However, additional strategies are needed for detailed and consistent observation and inference recording, enhancing the potential of such activities in favoring electrochemistry education in high schools.

Abstract The subject of chemical bonding is formal in nature and prone to the development of errors among students. This research was carried out at the beginning of the instruction on this subject in secondary education. It employed representations of the three types of chemical bonds to help students define and differentiate them. The participants were 27 students, from a 10th grade class in a school in Portugal, who faced conceptual and representational difficulties related to this topic. A pedagogical intervention, incorporating digital resources, was implemented to help them overcome these difficulties. Data collection instruments included a questionnaire (administered as both a pre-test and post-test), a post-class question, and an interview to gather students' opinions on the digital resources used, as well as the difficulties they experienced before and after learning this topic. The results show significant improvements in the second questionnaire compared to the first, although not all difficulties were overcome. Overall, students found the digital resources beneficial to their learning and offered suggestions for further improvement. This study underscores the importance of these resources in enhancing the teaching and learning of chemical bond representations and presents student feedback through interviews, providing firsthand insights into the effectiveness of these tools.

Abstract Iron is essential for the formation, maturation and dispersal of bacterial biofilms, playing a crucial role in the physiological and metabolic functions of bacteria as well as in the regulation of virulence. Limited availability of iron can impair the formation of robust biofilms by altering cellular motility, hydrophobicity and protein composition of the bacterial surface. In this study, the antibiofilm activity of two natural iron chelating agents, kojic acid (5-hydroxy-2-hydroxymethyl-4H-pyran-4-one) and maltol (3-hydroxy-2-methyl-4-pyrone), were investigated against Staphylococcus aureus and Pseudomonas aeruginosa. In addition, the ability of these 2-hydroxy-4-pyrone derivatives in preventing and eradicating S. aureus and P. aeruginosa biofilms through the enhancement of the efficacy of two antibiotics (tobramycin and ciprofloxacin) was explored. The iron binding capacity of the kojic acid and maltol was confirmed by their affinity for iron (III) which was found to be about 90 %, comparable to the regular chelating agent ethylenediaminetetraacetic acid (EDTA, 89 %). The antibiofilm efficacy of 2-hydroxy-4-pyrone derivatives, alone and in combination with antibiotics, was evaluated by measuring the total biomass, metabolic activity, and culturability of biofilm cells. Furthermore, their impact on the membrane integrity of S. aureus biofilm cells was investigated using flow cytometry and epifluorescence microscopy with propidium iodide staining. It was also examined the ability of 2-hydroxy-4-pyrone derivatives and 2-hydroxy-4-pyrone derivate-antibiotic dual-combinations in inhibiting the production of virulence factors (total proteases, lipases, gelatinases and siderophores) by S. aureus. Regarding biofilm formation, the results showed that 2-hydroxy-4-pyrone derivatives alone reduced the metabolic activity of S. aureus biofilm cells by over 40 %. When combined with tobramycin, a 2-log (CFU cm-2) reduction in S. aureus biofilm cells was observed. Moreover, the combination of maltol and kojic acid with ciprofloxacin prevented P. aeruginosa biomass production by 60 %, compared to 36 % with ciprofloxacin alone. In pre-established S. aureus and P. aeruginosa biofilms, selected compounds reduced the metabolic activity by over 75 %, and a 3-log (CFU cm-2) reduction in the culturability of biofilm cells was noted when kojic acid and maltol were combined with antibiotics. Moreover, 2-hydroxy-4-pyrone derivatives alone and in combination with tobramycin, damaged the cell membranes of pre-established biofilms and completely inhibited total proteases production. Despite the increasing of reactive oxygen species production caused by the cellular treatment of maltol, both 2-hydroxy-4-pyrone derivatives showed good safe profile when tested in human hepatocarcinoma (HepG2) cells. The pre-treatment of HepG2 cells with both compounds was crucial to prevent the cellular damage caused by iron (III). This study demonstrates for the first time that the selected 2-hydroxy-4-pyrone derivatives significantly enhance the antibiofilm activity of tested antibiotics against S. aureus and P. aeruginosa, highlighting their potential as antibiotic adjuvants in preventing and eradicating biofilm-related infections.

Abstract An extensive thermochemical study of gamma-undecanolactone and delta-undecanolactone has been developed using two complementary calorimetric techniques. The combustion energy of each compound was determined by static-bomb combustion calorimetry, and the corresponding enthalpy of vaporization was determined by high-temperature Calvet microcalorimetry, in which both properties of each compound are reported at T = 298.15 K. The standard molar enthalpy of formation in the gas phase of each lactone was derived by the combination of the experimental results. Additionally, high-level computational calculations were carried out, using composite ab initio G4 and G4(MP2) methods, as well as DFT M06-2X/6-311++G(d,p) approach, to estimate the corresponding enthalpy of formation in the gas phase. The experimental and computational results are in good agreement. The G4 and G4(MP2) methods show the best accordance with experimentally determined gas phase enthalpies of formation. The experimental results are discussed in terms of structural contributions to the energetic properties of the lactones studied, as well as to other alkylated gamma- and delta-lactones, and empirical correlations are suggested for the estimation of the standard molar enthalpies of formation, at T = 298.15 K, for other alkylated gamma- and delta-lactones, both in the liquid and gaseous phases, as well as for the respective enthalpies of vaporization. Finally, the thermochemistry of individual steps of lactone ring opening and successive decarboxylation mechanism, including the identification of transition states, was studied using the M06-2X/6-311++G(d,p) approach.

Abstract Borophene, a monolayer of boron atoms, belongs to intensively studied two-dimensional beyond-graphene materials. The B36 borophene nanoflake is a finite size model system, containing a hexagonal vacancy similar to the ones present in (312 and chi 3 borophene sheets. The hydrogen binding performance of B36 decorated with various transition metal atoms is investigated using density functional theory and quantum theory of atoms in molecules. Hydrogen is considered to become one of the crucial energy sources in future, hence, a search for effective hydrogen storage materials is of urge importance. Obtained results suggest that B36 decorated with Co, Ni, Fe, and Cu possess strong affinity to bind the H2 molecule via formation of eta 2-dihydrogen bonds. Among them, the strongest H2 binding is found for Co- and Ni-decorated B36. Furthermore, B36 decorated with Sc and Ti behave like H-H bond breakers while B36 decorated with Zn possess only negligible affinity to bind H2 molecule. The stability of the B36 decorated with Co and Ni is verified by ab initio molecular dynamics. The presented data may also serve as a basis for reference in future large-scale computational studies of borophene-based materials.

Abstract Heat capacity, a crucial physical property for chemical processes, is often understudied in Deep Eutectic Solvents (DESs), which in turn are promising green alternatives to environmentally hazardous conventional solvents. This work addresses this gap by developing a machine learning model to predict DES heat capacity and identify key structural features influencing it. We employed a dataset of 530 DESs with corresponding experimental heat capacity values. Quantum-chemical COSMO-RS-based descriptors, capturing detailed information about DES structures, were calculated for each data point. Various machine learning algorithms, namely k-Nearest Neighbours (kNN), Random Forests (RF), Neural Network Multilayer Perceptron (MLP), and Support Vector Machines (SVM) were explored alongside a linear model (Multiple Linear Regression, MLR). Hyperparameter optimisation ensured all models were fine-tuned for optimal performance. The most successful model, based on the MLP technique, achieved remarkably low Average Absolute Relative Deviation (AARD) values of 0.500 % and 3.999 % for the training and test sets, respectively. This signifies a significant improvement in prediction accuracy compared to traditional methods. Furthermore, by applying a SHapley Additive exPlanations (SHAP) analysis, we identified the most crucial structural factors within DES components that govern their heat capacity. This comprehensive investigation offers valuable insights that can pave the way for an efficient design of novel DESs in the future. © 2024 The Author(s)