Bimetallic Zeolitic Imidazolate Framework (ZIF)-derived N-doped Carbon Frameworks (M0.1Co0.9-NCF, M = Fe, Ni, Cu, Zn) were synthesized via a silica-templated pyrolysis route and evaluated for the selective hydrogenation of furfural (FAL) to furfuryl alcohol (FOL). The incorporation of different dopants into ZIF-67 enabled the formation of well-dispersed Co-M nanodomains embedded in hierarchical N-doped carbon matrices. Comprehensive characterization by XRD, TG-DTA, adsorption-desorption of nitrogen at −196 °C, XPS, SEM, TEM-EDS, CO2/NH3-TPD, ICP-OES, elemental analysis, and Raman spectroscopy revealed that the dopant nature governs metal dispersion, electron density, oxidation state distribution and the accessibility of Co surface species. These structural and electronic features directly determine catalytic performance. Among the investigated catalysts Fe0.1Co0.9-NCF exhibits the highest activity and selectivity, achieving 99.9% FAL conversion and 99.4% FOL selectivity at 100°C and 15 bar H2, followed by Ni0.1Co0.9-NCF (95% conversion and 97% selectivity). In contrast, Cu- and Zn-doped materials, characterized by electron-poor or partially encapsulated Co sites, favored acetalization or deeper hydrogenolysis pathways. The correlation of the catalytic features with the catalytic efficiency highlights the key role of electron-rich, surface accessible Co0/Co2+ ensembles and narrow dispersed nanodomains in promoting selective FAL hydrogenation.

Zeolitic imidazolate frameworks (ZIFs) have recently emerged as promising precursors for the synthesis of heteroatom-doped nanocarbon materials. The chemical and structural features of these frameworks are influenced by the synthesis methodology, which directly affects their catalytic efficiency and stability. This study aims to investigate such frameworks by exploring a Co-ZIF structure doped with iron. Part of the FexCoy-ZIF (x = 0.05-0.15; y = 0.95-0.85) precursors is directly pyrolyzed to form FexCoy-NPC (NPC-nanoporous carbon), while another part is coated with a silica shell, followed by the pyrolysis of the FexCoy-ZIF@SiO2 intermediates to produce FexCoy-NCF (NCF-nanocarbon framework). To elucidate their chemical, structural, and catalytic properties, the synthesized materials are comprehensively characterized and finally investigate in the base-free oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA). The optimal catalyst (Fe0.15Co0.85-NCF) demonstrates complete conversion of HMF (>99.9%) to FDCA with a pretty high selectivity (82.4%) after 6 h reaction at 80 °C. The correlation of the catalytic features with the efficiency of the catalysts provides insight into the catalytic characteristics responsible for the highest HMF conversion and selectivity to FDCA. The stability and recyclability of the catalysts are also examined.

Ruthenium-based catalysts were prepared through a deposition–precipitation approach, taking beta zeolites with Si/Al ratios of 12.5, 18.5, and 150, respectively, as supports, and 1–3 wt% loadings of metal. Their activation was performed in the presence of either H2 or NaBH4. The dispersion of the Ru species and the acid–base properties were influenced by both the preparation method and the activation protocol. The catalysts reduced under H2 flow presented well-dispersed Ru(0) and RuOx nanoparticles, while the reduction with NaBH4 led to larger RuOx crystallites and highly dispersed Ru(0). These characteristics exerted an important role in the hydrogenation of levulinic acid (LA) to γ-valerolactone (GVL). The H2 dissociation occurred via a heterolytic mechanism involving Lewis acid–base pairs associated with RuOx and the framework oxygen (Si-O-Al) located near the zeolite pore edge. The Ru(0) nanoparticles activated the –C=O bond of the LA substrate, while the presence of the carrier zeolite Brønsted acid sites promoted the ring-closure esterification of the 4-hydroxyvaleric acid (4-HVA) intermediate to GVL. An optimal combination of these features was achieved for the catalyst with 3 wt% Ru and a Si/Al ratio of 150, which selectively converted LA (XLA = 96.5%) to GVL (SGVL = 97.8%) at 130 °C and 10 bars of H2.

The primary objective of this research was to develop efficient solid catalysts that can directly convert the lactic acid (LA) obtained from lignocellulosic biomass into alanine (AL) through a reductive amination process. To achieve this, various catalysts based on ruthenium were synthesized using different carriers such as multi-walled carbon nanotubes (MWCNTs), beta-zeolite, and magnetic nanoparticles (MNPs). Among these catalysts, Ru/MNP demonstrated a remarkable yield of 74.0% for alanine at a temperature of 200 °C. This yield was found to be superior not only to the Ru/CNT (55.7%) and Ru/BEA (6.6%) catalysts but also to most of the previously reported catalysts. The characterization of the catalysts and their catalytic results revealed that metallic ruthenium nanoparticles, which were highly dispersed on the external surface of the magnetic carrier, significantly enhanced the catalyst’s ability for dehydrogenation. Additionally, the -NH2 basic sites on the catalyst further facilitated the formation of alanine by promoting the adsorption of acidic reactants. Furthermore, the catalyst could be easily separated using an external magnetic field and exhibited the potential for multiple reuses without any significant loss in its catalytic performance. These practical advantages further enhance its appeal for applications in the reductive amination of lactic acid to alanine.

The end of 2019 could be mounted in a rudimentary framing of a new medical problem, which globally introduces into the discussion a fulminant outbreak of coronavirus, consequently spreading COVID-19 that conducted long-lived and persistent repercussions. Hence, the theme proposed to be solved arises from the field of medical imaging, where a pulmonary CT-based standardized reporting system could be addressed as a solution. The core of it focuses on certain impediments such as the overworking of doctors, aiming essentially to solve a classification problem using deep learning techniques, namely, if a patient suffers from COVID-19, viral pneumonia, or is healthy from a pulmonary point of view. The methodology's approach was a meticulous one, denoting an empirical character in which the initial stage, given using data processing, performs an extraction of the lung cavity from the CT scans, which is a less explored approach, followed by data augmentation. The next step is comprehended by developing a CNN in two scenarios, one in which there is a binary classification (COVID and non-COVID patients), and the other one is represented by a three-class classification. Moreover, viral pneumonia is addressed. To obtain an efficient version, architectural changes were gradually made, involving four databases during this process. Furthermore, given the availability of pre-trained models, the transfer learning technique was employed by incorporating the linear classifier from our own convolutional network into an existing model, with the result being much more promising. The experimentation encompassed several models including MobileNetV1, ResNet50, DenseNet201, VGG16, and VGG19. Through a more in-depth analysis, using the CAM technique, MobilneNetV1 differentiated itself via the detection accuracy of possible pulmonary anomalies. Interestingly, this model stood out as not being among the most used in the literature. As a result, the following values of evaluation metrics were reached: loss (0.0751), accuracy (0.9744), precision (0.9758), recall (0.9742), AUC (0.9902), and F1 score (0.9750), from 1161 samples allocated for each of the three individual classes.

Hydrogels are a viable option for biomedical applications due to their biocompatibility, biodegradability, and ability to incorporate various healing agents while maintaining their biological efficacy. This study focused on the preparation and characterization of novel hybrid hydrogels enriched with the natural algae compound Ulvan for potential use in wound dressings. The characterization of the hydrogel membranes involved multiple methods to assess their structural, mechanical, and chemical properties, such as pH measurements, swelling, moisture content and uptake, gel fraction, hydrolytic degradation, protein adsorption and denaturation tests, rheological measurements, SEM, biocompatibility testing, and scratch wound assay. The hydrogel obtained with a higher concentration of Ulvan (1 mg/mL) exhibited superior mechanical properties, a swelling index of 264%, a water content of 55%, and a lower degradation percentage. In terms of rheological properties, the inclusion of ULV in the hydrogel composition enhanced gel strength, and the Alginate + PVA + 1.0ULV sample demonstrated the greatest resistance to deformation. All hydrogels exhibited good biocompatibility, with cell viability above 70% and no obvious morphological modifications. The addition of Ulvan potentiates the regenerative effect of hydrogel membranes. Subsequent studies will focus on encapsulating bioactive compounds, investigating their release behavior, and evaluating their active biological effects.

Carbon quantum dots (CQDs) are widely investigated as an enhancing photocatalytic component of various nanocomposites. With this aim hetero-structures containing CQDs associated to metal oxides were prepared following a hydrothermal approach in which commercial ZnO and TiO2 P-25 Degussa were used as carriers. CQDs were synthesized in advance by a low-temperature hydrothermal (LHT) treatment of useless humins wastes produced by the glucose dehydration in an acidic medium. Their photocatalytic behavior was investigated in the HMF selective oxidation. The obtained results revealed electronic interactions between CQDs and MOx (i.e., ZnO and TiO2) which have as effect the enhancement of the charge separation and diminution of the charge recombination. The influence of CQDs and buffer addition on the products distribution was evaluated. The highest photocatalytic efficiency corresponded to the TiO2/CQD180-12 heterostructure: under UV irradiation FDCA was produced in 360 min with a selectivity of 35.5% for a conversion of 93.4% of HMF.

In recent years, metal-organic frameworks (MOFs) have significantly contributed to broadening the frontiers of science. Due to their distinctive properties including well-developed surface area, high porosity, multifarious composition, tunable and uniform pore structures, and comprehensive functionality, they were applied in different fields such as separation, drug delivery, fuel storage, chemical sensing, and catalysis. The application of pristine MOFs as materials that speed up the reaction rate could be restricted mainly because of the limited number of active sites and their low mechanical and thermal stability. In order to enhance their catalytic properties, metalorganic frameworks can be functionalized or integrated with a variety of materials to obtain composites or hybrids. The review outlines the state of art concerning the application of MOFs and their composites as catalysts in various organic transformation processes. A particular focus was given to the oxidation of alkanes, cycloalkanes, alkylbenzenes, alcohols, thiols, sulfides. Furthermore, the role of metalorganic frameworks in hydrogenation and C–C coupling reactions were also presented.

Cationic transition metal (Cu and Co) based core-shell (core: magnetite, shell: SiO2, ZrO2 or CeO2) magnetic nanoparticles were investigated in the selective oxidation of D-glucose to gluconic (GLU) and glucuronic (GLUU) acids, working at low temperature, with H2O2 as oxidant and in the absence of any base in the liquid phase. The catalysts have been characterized by FTIR, XRD, NH3- and CO2-TPD and TGA-DTA analyses. Under the optimal reaction conditions (i.e., 40 degrees C and 10 h), the highest conversion of D-glucose was of 86.2 % and the selectivities to GLU and GLUU of 33.8 % and 14.6 %, respectively. These results corresponded to the MSA@Co (were: M - magnetite; S - silica shell, A -APTES) catalyst. This catalyst can be easily recovered by a simple external magnet and reused several consecutive runs, without any change in the catalytic efficiency.

The selective oxidation of 5-hydroxymethylfurfural (HMF) to 5-hydroxymethyl-2-furancarboxylic acid (HMFCA) has been investigated on Co-based MOF catalysts. ZIF-67 (ZIF, Zeolitic Imidazolate Framework), Co-BTC and Co-BTC-2mim were solvothermally synthesized by the association of cobalt with 2-methylimidazole (2-mim), 1,3,5-benzenetricarboxylic acid (H3BTC), or a mixture of the two linkers. Besides these, a novel core-shell Co3O4 @Co-BTC structure with a core of Co3O4 and Co-BTC on its outer layer has also been synthesized by a simple replacement of the cobalt nitrate precursor with Co3O4. Among the prepared materials the core-shell Co3O4 @CoBTC catalyst afforded the proper catalytic features (i.e., specific density of the acid and base site combined with the high accessibility of the HMF molecules to the active Co sites) for a high selectivity to HMFCA (89%) at a HMF conversion (X-HMF) of 79%, at 60 degrees Cand after only 1 h, in the presence of t-BuOOH as oxidant and acetonitrile as solvent.

The catalytic hydrotreatment of humins, the solid byproduct produced from the conversion of C6 sugars (glucose, fructose) to 5-hydroxymethylfurfural (HMF), using supported Pd@zeolite (Beta, Y, and USY) catalysts with different amounts of Pd (i.e., 0.5, 1.0 and 1.5 wt%) was investigated under molecular hydrogen pressure. The highest conversion of humins (52.0%) was obtained on 1.5Pd@USY catalyst while the highest amount of humins oil (27.3%) was obtained in the presence of the 1Pd@Beta zeolite sample, at PH2 = 30 bars and T = 250 °C. The major compounds in the humins oil evidenced by GC-MS are alcohols, organic acids, ethers, and alkyl-phenolics. However, although all these classes of compounds are obtained regardless of the nature of the catalyst used, the composition of the mixture differs from one catalyst to another. Furanic compounds were not identified in the reaction products. A possible explanation may be related to their high reactivity under the reaction conditions, in the presence of the Pd-based catalysts these compounds lead to alkyl phenolics, important intermediates in the petrochemical industry.

Considerable efforts have been made in recent years to identify an optimal treatment method for the removal of antibiotics from wastewaters. A series of supramolecular organic-inorganic magnetic composites containing Zn-modified MgAl LDHs and Cu-phthalocyanine as photosensitizers were prepared with the aim of removing β-lactam antibiotics from aqueous solutions. The characterization of these materials confirmed the anchorage of Cu-phthalocyanine onto the edges of the LDH lamellae, with a negligible part inserted in the interlayer space. The removal of the β-lactam antibiotics occurred via concerted adsorption and photocatalytic degradation. The efficiency of the composites depended on (i) the LDH: magnetic nanoparticle (MP) ratio, which was strongly correlated with the textural properties of the catalysts, and (ii) the phthalocyanine loading in the final composite. The maximum efficiency was achieved with a removal of ~93% of the antibiotics after 2 h of reaction.

The green macroalga Ulva rigida represents a promising feedstock for biorefinary due to its fast growth and cosmopolitan distribution. The main component of the cell walls of U. rigida is a sulfated glucuronorhamnan polysaccharide known as ulvan. Herein it was found that due to the high (hydrogen)sulfate group content of ulvan, hydrothermal autohydrolysis at 130 degrees C renders a high percentage of rhamnose (78-79 % recovery from the initial content in the raw material), a rare sugar of high added value. In addition, acid catalysis by a triflate-based graphene oxide under oxygen-free conditions at 180 degrees C affords moderate amounts of tartaric acid (24-26 %). The same triflate-based graphene oxide catalyst under oxygen pressure yields remarkably high percentages of succinic acid (65 %). The catalyst preserves its activity for at least five consecutive reuses.

Currently, carbon quantum dots (CQDs) have been widely investigated as an enhancing photocatalytic component of various nanocomposites. In this study, hetero-structures containing carbon quantum dots (CQDs) associated to zinc oxide were prepared following two one-pot procedures: (i) a hydrothermal approach in which commercial ZnO was used as carrier for CQDs; and (ii) an approach in which the ZnO/CQDs samples were produced in situ by adding zinc acetate to an aqueous suspension of CQDs. CQDs were prepared in advance by a low-temperature hydrothermal (LHT) treatment of useless humins wastes produced by the glucose dehydration in an acidic medium. These samples were characterized by several techniques such asadsorption-desorption isotherms of liquid nitrogen at 77K, X-ray diffraction (XRD), infrared diffuse reflectance with Fourier transform (DRIFT) and UV-vis spectroscopy. The photocatalytic behavior of these materials was investigated in the degradation of methylene blue (MB). The obtained results revealed electronic interactions between CQDs and ZnO which have as an effect an enhancement of the charge separation and diminution of the charge recombination. In accordance, a correlation between the photocatalytic activity and the intrinsic properties of ZnO/CQDs has been evidenced. The highest photocatalytic activity corresponded to the heterostructure containing highly dispersed narrow sized CQDs onto ZnO. Under visible light irradiation and after 180 min of irradiation, MB was degraded by as much as 97.6%.

The industrial environment has gone through the fourth revolution, also called "Industry 4.0", where the main aspect is digitalization. Each device employed in an industrial process is connected to a network called the industrial Internet of things (IIOT). With IIOT manufacturers being capable of tracking every device, it has become easier to prevent or quickly solve failures. Specifically, the large amount of available data has allowed the use of artificial intelligence (AI) algorithms to improve industrial applications in many ways (e.g., failure detection, process optimization, and abnormality detection). Although data are abundant, their access has raised problems due to privacy concerns of manufacturers. Censoring sensitive information is not a desired approach because it negatively impacts the AI performance. To increase trust, there is also the need to understand how AI algorithms make choices, i.e., to no longer regard them as black boxes. This paper focuses on recent advancements related to the challenges mentioned above, discusses the industrial impact of proposed solutions, and identifies challenges for future research. It also presents examples related to privacy-preserving and explainable AI solutions, and comments on the interaction between the identified challenges in the conclusions.

The oxidation of 5-hydroxymethylfurfural (HMF) to 5-hydroxymethyl-2-furancarboxylic acid (HMFCA) has been investigated on MOx@Nb-zeolite catalytic systems (where MOx = CoOx, MnOx or FeOx and zeolite -USY and Beta) with hierarchical texture and acid-base properties. These catalysts were exhaustively characterized by several techniques such as XRD, adsorption-desorption of liquid nitrogen at 77 K, CO2- and NH3-TPD, DRIFT, XPS, and SS NMR. The new hierarchically catalysts produced HMFCA with a selectivity of 97.0% for a XHMF = 95.0% (Mn@Nb-Y30). A synergetic effect between MOx and Nb species in the activation of t-BuOOH and oxidation of HMF has been identified. The absence of FDCA in the reaction products using MOx@Nb-zeolite catalysts can be due to the preponderant presence of the acidic sites (associated to both framework and extra framework AlOx) which prevent a further oxidation of HMFCA to FDCA.

The goals of this paper are: (a) to investigate adaptive and fractional-order adaptive control algorithms for an automatic anesthesia process, using a closed-loop system, and (b) to develop an easy-to-use tool for MATLAB/Simulink to facilitate simulations for users with less knowledge about anesthesia and adaptive control. A model reference adaptive control structure was chosen for the entire system. First of all, to control the patient’s state during the surgery process, the patient mathematical model is useful, or even required for simulation studies. The pharmacokinetic/pharmacodynamics (PK/PD) model was determined using MATLAB’s SimBiology tool, starting from a previously available block diagram, and validated through simulation. Then, to achieve the desired control performances, two controllers are designed: a PI adaptive controller and a PI λ (PI-fractional) adaptive controller, using the MIT algorithm. The time response during anesthetic drug infusion for each patient can be plotted with the AnesthesiaGUIDE tool, which is also designed in MATLAB/Simulink. The tool was tested on data from 12 patients, subjected to general anesthesia, with successful results. Through this tool, the article provides a good opportunity for any user to experience with adaptive control for the anesthesia process.

Background Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular -omics data from clinical data warehouses and biobanks. Methods The European “ITFoC (Information Technology for the Future Of Cancer)” consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the “ITFoC Challenge”. This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care.

Highly dispersed supported NbOx species were prepared via a deposition precipitation-carbonization (DPC)-like method. As precursors for niobium species and carrier ammonium niobate(V) oxalate hydrate and humins were used. Characterization of the resulted catalysts indicated bi-functional acid-base niobium species anchored onto a highly hydrophobic graphite-like carbon structure. These catalysts were investigated in the one-pot conversion of glucose to 5-hydroxymethylfurfural (HMF) in a biphasic system consisting of a mixture of a 20 wt% NaCl aqueous solution phase and an organic extracting phase (methyl-isobutyl-ketone (MIBK), 2-tert-butylphenol (TBP) or 2sec-butylphenol (SBP)). The optimization conditions afforded the highest yield to HMF (96 %) for the GHNb1.2 catalyst (with 2.5 wt%Nb), the base/acid sites ratio of 1.76, in biphasic TBP/water system, at 180 ?C after 8 h.