Hydrolytic zinc enzymes are common targets for protein design. The versatility of the zinc chemistry can be combined with the usage of small protein scaffolds for biocatalytic applications. Despite this, the computational design of metal-containing proteins remains challenging due to the need to properly model protein–metal interactions. We addressed these issues by developing a computational multi-state design approach of artificial zinc hydrolases based on small protein scaffolds. The zinc-finger peptide Sp1f2 was redesigned to accommodate a catalytic zinc centre and the villin headpiece C-terminal subdomain HP35 was de novo designed for metal-binding and catalytic activity. Both metallopeptides exhibited metal-induced folding (KZnP,app ≈ 2 × 105 M−1) and hydrolytic activity (k2 ≈ 0.1 M−1 s−1) towards an ester substrate. By focusing on the inherent flexibility of small proteins and their interactions with the metal ion by molecular dynamics simulations and spectroscopic studies, we identified current limitations on computational design of metalloenzymes and propose how these can be overcome by integrating information of protein–metal interactions in long time scale simulations.

Antimicrobial resistance is one of the most worrying threats to humankind with extremely high healthcare costs associated. The current technologies used in clinical microbiology to identify the bacterial agent and profile antimicrobial susceptibility are time‐consuming and frequently expensive. As a result, physicians prescribe empirical antimicrobial therapies. This scenario is often the cause of therapeutic failures, causing higher mortality rates and healthcare costs, as well as the emergence and spread of antibiotic resistant bacteria. As such, new technologies for rapid identification of the pathogen and antimicrobial susceptibility testing are needed. This review summarizes the current technologies, and the promising emerging and future alternatives for the identification and profiling of antimicrobial resistance bacterial agents, which are expected to revolutionize the field of clinical diagnostics.

The development of gas sensing materials is relevant in the field of non-invasive biodevices. In this work, we used an electronic nose (E-nose) developed by our research group, which possess versatile and unique sensing materials. These are gels that can be spread over the substrate by Film Coating or Spin Coating. This study aims to evaluate the influence of the sensing film spreading method selected on the classification capabilities of the E-nose. The methodology followed consisted of performing an experiment where the E-nose was exposed to 13 different pure volatile organic compounds. The sensor array had two sensing films produced by Film Coating, and other two produced by Spin Coating. After data collection, a set of features was extracted from the original signal curves, and the best were selected by Recursive Feature Elimination. Then, the classification performance of Multinomial Logistic regression, Decision Tree, and Naíve Bayes was evaluated. The results showed that both s preading methods for sensing film’s production are adequate since the estimated error of classification was inferior to 4 % for all the classification tools applied.

A (model) composite system for drug delivery was developed based on a thermoresponsive hydrogel loaded with microparticles. We used Pluronic F127 hydrogel as the continuous phase and alginate microparticles as the dispersed phase of this composite system. It is well known that Pluronic F127 forms a gel when added to water in an appropriate concentration and in a certain temperature range. Pluronic F127 hydrogel may be loaded with drug and injected, in its sol state, to act as a drug delivery system in physiological environment. A rheological characterization allowed the most appropriate concentration of Pluronic F127 (15.5 wt%) and appropriate alginate microparticles contents (5 and 10 wt%) to be determined. Methylene blue (MB) was used as model drug to perform drug release studies in MB loaded Pluronic hydrogel and in MB loaded alginate microparticles/Pluronic hydrogel composite system. The latter showed a significantly slower MB release than the former (10 times), suggesting its potential in the development of dual cargo release systems either for drug delivery or tissue engineering.

Polymeric parts produced by Fused Deposition Modelling (FDM) Additive Manufacturing (AM) has no special safety requirements, and therefore, NDT is not required. However, the use of AM to produce Fibre Reinforcement Thermoplastics (FRTP) parts means that structural applications with safety requirements are envisaged, demanding reliable NDT methods. This paper presents experimental results and numerical simulation by Finite Element Method (FEM) of the NDT inspection of different parts of polymeric and RFTP composite materials. The parts were produced by FDM Additive Manufacturing and different delamination defects were introduced at different positions and with different dimensions and morphologies. Two different NDT techniques were used, exploiting different inspection parameters: air-coupled ultrasound, using frequencies between 50 and 400 kHz and active transient thermography, in both reflection and transition modes. The influence of the curvature of the parts was analysed, from the experimental point of view, and the results were compared with different numerical simulation strategies. It was shown that, both NDT techniques can detect the defects, with good spatial resolution, being the thermography reflection mode the fastest and expedite for curvature parts. The numerical simulation corroborates the experimental results allowing a deeper insight on the physical phenomena involved.

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ORIGINAL RESEARCH ARTICLE
Front. Chem., 14 November 2019 | https://doi.org/10.3389/fchem.2019.00756
Mesoporous Silica vs. Organosilica Composites to Desulfurize Diesel
Susana O. Ribeiro1, Carlos M. Granadeiro1, Marta C. Corvo2, João Pires3, José M. Campos-Martin4, Baltazar de Castro1 and Salete S. Balula1*
1LAQV-REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade Do Porto, Porto, Portugal
2CENIMAT/I3N, Departamento de Ciência dos Materiais, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
3Faculdade de Ciências, Centro de Química e Bioquímica and CQE, Universidade de Lisboa, Lisbon, Portugal
4Grupo de Energía y Química Sostenibles (EQS), Instituto de Catálisis y Petroleoquímica, CSIC, Madrid, Spain
The monolacunary Keggin-type [PW11O39]7− (PW11) heteropolyanion was immobilized on porous framework of mesoporous silicas, namely SBA-15 and an ethylene-bridged periodic mesoporous organosilica (PMOE). The supports were functionalized with a cationic group (N-trimethoxysilypropyl-N, N, N-trimethylammonium, TMA) for the successful anchoring of the anionic polyoxometalate. The PW11@TMA-SBA-15 and PW11@TMA-PMOE composites were evaluated as heterogeneous catalysts in the oxidative desulfurization of a model diesel. The PW11@TMA-SBA-15 catalyst showed a remarkable desulfurization performance by reaching ultra-low sulfur levels (<10 ppm) after only 60 min using either a biphasic extractive and catalytic oxidative desulfurization (ECODS) system (1:1 MeCN/diesel) or a solvent-free catalytic oxidative desulfurization (CODS) system. Furthermore, the mesoporous silica composite was able to be recycled for six consecutive cycles without any apparent loss of activity. The promising results have led to the application of the catalyst in the desulfurization of an untreated real diesel supplied by CEPSA (1,335 ppm S) using the biphasic system. The system has proved to be a highly efficient process by reaching desulfurization values higher than 90% for real diesel during three consecutive cycles.

Ionic liquids have been on the spotlight of chemical research field in the last decades. Their physical properties (low vapor pressure, thermal stability, and conductivity) and the possibility of fine tuning make them a versatile class of compounds for a wide range of applications, such as catalysis, energy, and material sciences. Ionic liquids can establish multiple intermolecular interactions with solutes such as electrostatic, van der Waals, or hydrogen bonds. The prospect of designing ionic liquid structures toward specific applications has attracted the attention to these alternative solvents. However, their rational design demands a molecular detailed view, and Nuclear Magnetic Resonance is a unique and privileged technique for this purpose, as it provides atomic resolution and at the same time enables the study of dynamic information. In this chapter, we provide an overview about the application of Nuclear Magnetic Resonance spectroscopy techniques as a methodology for the rational design of ionic liquids as solvents for small organic compounds, CO2 capture, and polymers such as cellulose focusing mainly in the last 10 years.

Extracts from plants have considerable significance as bioactive compounds with several pharmacological applications. Polyphenols have attracted the attention as anti-inflammatory and anti-oxidative materials. Nonetheless, the amount of these compounds in the extracts is typically very low. Consequently, green extraction techniques with higher efficiency for phenolic compounds are of paramount importance. Ionic liquids (ILs), which are also known as designer solvents can be used to extract polyphenols, however the search for ideal solvents is mostly done by trial and error. In this work, nuclear magnetic resonance (NMR) is used to study the profile of ILs molecular interactions with model compounds that mimic polyphenols. The ILs that exhibit the strongest molecular interactions were proven to have the highest efficiency when extracting polyphenols from matcha Japanese green tea, known to be extremely rich in these compounds. Both the IL cation and anion have an influence on the solvent behaviour. The best IL solvents for matcha polyphenols were imidazolium derivatives with shorter alkyl side chains and weakly basic anions such as tricyanomethanide, dicyanamide and triflate. Thus, the NMR approach avoids an exhaustive testing and allows the rational selection of the best ILs for the extraction.

The use of non-destructive evaluation (NDE) techniques for assessing microstructural changes in processed materials is of particular importance as it can be used to assess, qualitatively, the integrity of any material/structure. Among the several NDE techniques available, electrical conductivity measurements using eddy currents attract great attention owing to its simplicity and reliability. In this work, the electrical conductivity profiles of friction stir processed Ti6Al4V, Cu, Pb, S355 steel and gas tungsten arc welded AISI 304 stainless steel were determined through eddy currents and four-point probe. In parallel, hardness measurements were also performed. The profiles matched well with the optical macrographs of the materials: while entering in the processed region a variation in both profiles was always observed. One particular advantage of electrical conductivity profiles over hardness was evident: it provides a better resolution of the microstructural alterations in the processed materials. Moreover, when thermomechanical processing induces microstructural changes that modify the magnetic properties of a material, eddy currents testing can be used to qualitatively determine the phase fraction in a given region of the material. A qualitative relation between electrical conductivity measurements and hardness is observed.

Bacterial infections affect about 1 in 5 patients who receive a dental implant within 5 years of surgery. To avoid the implant rejection it is necessary for the development of innovative biomaterials, with addition or substitution of the ions, for implant coatings that promote a strong bond with the new host bone and antibacterial action. The objective of this work was to synthesize a bioactive glass with different silver concentrations to evaluate their antibacterial performance. The glasses were synthesized with up to 2% silver content by melt-quenching. Structural, morphological, biological, and electrical properties of all samples were studied. The biological behavior was evaluated through cytotoxicity tests and antibacterial activity. The structural analysis shows that the introduction of silver do not promote significant changes, not altering the advantageous properties of the bioglass of the bioglass. It was verified that the glasses with a silver content from 0.5% to 2%, completely prevented the growth of both Staphylococcus aureus and Escherichia coli while being nontoxic toward mammalian cells. Therefore, these bioglasses are promising materials to be used in the production of dental implants with antimicrobial activity.

The demand for better cellulose solvents has driven the search for new and improved materials to enable the processing of this polysaccharide. Ionic liquids have been debated for a long time as interesting alternatives, but the molecular details on the solubilization mechanism have been a matter of controversy. Herein, for the first time, the structure and dynamics of hydroxypropylcellulose (HPC) liquid crystal solutions were probed in the presence of imidazolium ionic liquids (ILs), conjugating rheological measurements with magnetic resonance spectroscopy. This study provides a characterization of the solutions macroscopic behaviour, where the liquid crystalline (LC) properties were maintained. Using ILs with different side chain lengths, the influence of the hydrophobic IL domain in the solvation abilities of ILs towards a cellulose derived polymer was accessed, providing experimental evidence on these interactions.

Electronic noses (E-noses), are usually composed by an array of sensors with different selectivities towards classes of VOCs (Volatile Organic Compounds). These devices have been applied to a variety of fields, including environmental protection, public safety, food and beverage industries, cosmetics, and clinical diagnostics. This work demonstrates that it is possible to classify eleven VOCs from different chemical classes using a single gas sensing biomaterial that changes its optical properties in the presence of VOCs. To accomplish this, an in-house built E-nose, tailor-made for the novel class of gas sensing biomaterials, was improved and combined with powerful machine learning techniques. The device comprises a delivery system, a detection system and a data acquisition and control system. It was designed to be stable, miniaturized and easy-to-handle. The data collected was pre-processed and features and curve fitting parameters were extracted from the original response. A recursive feature selection method was applied to select the best features, and then a Support Vector Machine classifier was implemented to distinguish the eleven distinct VOCs. The results show that the followed methodology allowed the classification of all the VOCs tested with 94.6% (± 0.9%) accuracy.