Simplified model for prediction of compressive strength of alkali-activated natural pozzolans

The assessment of pozzolanic activity is essential for estimating the reaction of a material as pozzolan. Natural pozzolans can be activated and condensed with sodium silicate in an alkaline environment to synthesize high performance cementitious construction materials with low environmental impact. In this paper, the pozzolanic activities of five natural pozzolans are studied. The correlation between type and chemical composition of natural pozzolan, which affects the formation of the geopolymer gel phase, both for the calcined and untreated natural pozzolans, have been reviewed. The improvement in pozzolanic properties was studied following heat treatment including calcinations and/or elevated curing temperature by using alkali solubility, and compressive strength tests. A model was developed to allow prediction of the alkali-activated pozzolan strength versus their chemical compositions, alkali solubility, and crystallinity.

Shift from coral to macroalgae dominance on a volcanically acidified reef

Rising anthropogenic CO2 in the atmosphere is accompanied by an increase in oceanic CO2 and a concomitant decline in seawater pH (ref. 1). This phenomenon, known as ocean acidification (OA), has been experimentally shown to impact the biology and ecology of numerous animals and plants2, most notably those that precipitate calcium carbonate skeletons, such as reef-building corals3. Volcanically acidified water at Maug, Commonwealth of the Northern Mariana Islands (CNMI) is equivalent to near-future predictions for what coral reef ecosystems will experience worldwide due to OA. We provide the first chemical and ecological assessment of this unique site and show that acidification-related stress significantly influences the abundance and diversity of coral reef taxa, leading to the often-predicted shift from a coral to an algae-dominated state4, 5. This study provides field evidence that acidification can lead to macroalgae dominance on reefs.

Application of gas hydrate equilibria prediction in process engineering

Desde hace cerca de dos siglos, los hidratos de gas han ganado un rol importante en la ingeniería de procesos, debido a su impacto económico y ambiental en la industria -- Cada día, más compañías e ingenieros ganan interés en este tema, a medida que nuevos desafíos muestran a los hidratos de gas como un factor crucial, haciendo su estudio una solución para un futuro próximo -- Los gases de hidrato son estructuras similares al hielo, compuestos de moléculas huéspedes de agua conteniendo compuestos gaseosos -- Existen naturalmente en condiciones de presiones altas y bajas temperaturas, condiciones típicas de algunos procesos químicos y petroquímicos [1] -- Basado en el trabajo doctoral de Windmeier [2] y el trabajo doctoral the Rock [3], la descripción termodinámica de las fases de los hidratos de gas es implementada siguiendo el estado del arte de la ciencia y la tecnología -- Con ayuda del Dortmund Data Bank (DDB) y el paquete de software correspondiente (DDBSP) [26], el desempeño del método fue mejorado y comparado con una gran cantidad de datos publicados alrededor del mundo -- También, la aplicabilidad de la predicción de los hidratos de gas fue estudiada enfocada en la ingeniería de procesos, con un caso de estudio relacionado con la extracción, producción y transporte del gas natural -- Fue determinado que la predicción de los hidratos de gas es crucial en el diseño del proceso del gas natural -- Donde, en las etapas de tratamiento del gas y procesamiento de líquido no se presenta ninguna formación, en la etapa de deshidratación una temperatura mínima de 290.15 K es crítica y para la extracción y transporte el uso de inhibidores es esencial -- Una composición másica de 40% de etilenglicol fue encontrada apropiada para prevenir la formación de hidrato de gas en la extracción y una composición másica de 20% de metanol en el transporte

Coupling of Chemical Kinetics with Computational Fluid Dynamics in a Three-Dimensional Engine Model

The role of computer modeling has grown recently to integrate itself as an inseparable tool to experimental studies for the optimization of automotive engines and the development of future fuels. Traditionally, computer models rely on simplified global reaction steps to simulate the combustion and pollutant formation inside the internal combustion engine. With the current interest in advanced combustion modes and injection strategies, this approach depends on arbitrary adjustment of model parameters that could reduce credibility of the predictions. The purpose of this study is to enhance the combustion model of KIVA, a computational fluid dynamics code, by coupling its fluid mechanics solution with detailed kinetic reactions solved by the chemistry solver, CHEMKIN. As a result, an engine-friendly reaction mechanism for n-heptane was selected to simulate diesel oxidation. Each cell in the computational domain is considered as a perfectly-stirred reactor which undergoes adiabatic constant- volume combustion. The model was applied to an ideally-prepared homogeneous- charge compression-ignition combustion (HCCI) and direct injection (DI) diesel combustion. Ignition and combustion results show that the code successfully simulates the premixed HCCI scenario when compared to traditional combustion models. Direct injection cases, on the other hand, do not offer a reliable prediction mainly due to the lack of turbulent-mixing model, inherent in the perfectly-stirred reactor formulation. In addition, the model is sensitive to intake conditions and experimental uncertainties which require implementation of enhanced predictive tools. It is recommended that future improvements consider turbulent-mixing effects as well as optimization techniques to accurately simulate actual in-cylinder process with reduced computational cost. Furthermore, the model requires the extension of existing fuel oxidation mechanisms to include pollutant formation kinetics for emission control studies.

Human Ezrin: Prediction of the 3D structure and interactions with mTOR

The protein Ezrin, is a member of the ERM family (Ezrin, Radixin and Moesin) that links the F-actin to the plasma membrane. The protein is made of three domains namely the FERM domain, a central α-helical domain and the CERMAD domain. The residues in Ezrin such as Ser66, Tyr145, Tyr353 and Tyr477 regulate the function of the protein through phosphorylation. The protein is found in two distinct conformations of active and dormant (inactive) state. The initial step during the conformation change is the breakage of intramolecular interaction in dormant Ezrin by phosphorylation of residue Thr567. The dormant structure of human Ezrin was predicted computationally since only partial active form structure was available. The validation analysis showed that 99.7% residues were positioned in favored, allowed and generously allowed regions of the Ramachandran plot. The Z-score of Ezrin was −7.36, G-factor was 0.1, and the QMEAN score of the model was 0.61 indicating a good model for human Ezrin. The comparison of the conformations of the activated and dormant Ezrin showed a major shift in the F2 lobe (residues 142-149 and 161-177) while changes in the conformation induced mobility shifts in lobe F3 (residues 261 to 267). The 3D positions of the phosphorylation sites Tyr145, Tyr353, Tyr477, Tyr482 and Thr567 were also located. Using targeted molecular dynamic simulation, the molecular movements during conformational change from active to dormant were visualized. The dormant Ezrin auto-inhibits itself by a head-to-tail interaction of the N-terminal and C-terminal residues. The trajectory shows the breakage of the interactions and mobility of the CERMAD domain away from the FERM domain. Protein docking and clustering analysis were used to predict the residues involved in the interaction between dormant Ezrin and mTOR. Residues Tyr477 and Tyr482 were found to be involved in interaction with mTOR.

Prediction of Upward Flame Spread over Polymers

In this work, the existing understanding of flame spread dynamics is enhanced through an extensive study of the heat transfer from flames spreading vertically upwards across 5 cm wide, 20 cm tall samples of extruded Poly (Methyl Methacrylate) (PMMA). These experiments have provided highly spatially resolved measurements of flame to surface heat flux and material burning rate at the critical length scale of interest, with a level of accuracy and detail unmatched by previous empirical or computational studies. Using these measurements, a wall flame model was developed that describes a flame’s heat feedback profile (both in the continuous flame region and the thermal plume above) solely as a function of material burning rate. Additional experiments were conducted to measure flame heat flux and sample mass loss rate as flames spread vertically upwards over the surface of seven other commonly used polymers, two of which are glass reinforced composite materials. Using these measurements, our wall flame model has been generalized such that it can predict heat feedback from flames supported by a wide range of materials. For the seven materials tested here – which present a varied range of burning behaviors including dripping, polymer melt flow, sample burnout, and heavy soot formation – model-predicted flame heat flux has been shown to match experimental measurements (taken across the full length of the flame) with an average accuracy of 3.9 kW m-2 (approximately 10 – 15 % of peak measured flame heat flux). This flame model has since been coupled with a powerful solid phase pyrolysis solver, ThermaKin2D, which computes the transient rate of gaseous fuel production of constituents of a pyrolyzing solid in response to an external heat flux, based on fundamental physical and chemical properties. Together, this unified model captures the two fundamental controlling mechanisms of upward flame spread – gas phase flame heat transfer and solid phase material degradation. This has enabled simulations of flame spread dynamics with a reasonable computational cost and accuracy beyond that of current models. This unified model of material degradation provides the framework to quantitatively study material burning behavior in response to a wide range of common fire scenarios.

Characterization of Non-linear Polymer Properties to Predict Process Induced Warpage and Residual Stress of Electronic Packages

Nonlinear thermo-mechanical properties of advanced polymers are crucial to accurate prediction of the process induced warpage and residual stress of electronics packages. The Fiber Bragg grating (FBG) sensor based method is advanced and implemented to determine temperature and time dependent nonlinear properties. The FBG sensor is embedded in the center of the cylindrical specimen, which deforms together with the specimen. The strains of the specimen at different loading conditions are monitored by the FBG sensor. Two main sources of the warpage are considered: curing induced warpage and coefficient of thermal expansion (CTE) mismatch induced warpage. The effective chemical shrinkage and the equilibrium modulus are needed for the curing induced warpage prediction. Considering various polymeric materials used in microelectronic packages, unique curing setups and procedures are developed for elastomers (extremely low modulus, medium viscosity, room temperature curing), underfill materials (medium modulus, low viscosity, high temperature curing), and epoxy molding compound (EMC: high modulus, high viscosity, high temperature pressure curing), most notably, (1) zero-constraint mold for elastomers; (2) a two-stage curing procedure for underfill materials and (3) an air-cylinder based novel setup for EMC. For the CTE mismatch induced warpage, the temperature dependent CTE and the comprehensive viscoelastic properties are measured. The cured cylindrical specimen with a FBG sensor embedded in the center is further used for viscoelastic property measurements. A uni-axial compressive loading is applied to the specimen to measure the time dependent Young’s modulus. The test is repeated from room temperature to the reflow temperature to capture the time-temperature dependent Young’s modulus. A separate high pressure system is developed for the bulk modulus measurement. The time temperature dependent bulk modulus is measured at the same temperatures as the Young’s modulus. The master curve of the Young’s modulus and bulk modulus of the EMC is created and a single set of the shift factors is determined from the time temperature superposition. The supplementary experiments are conducted to verify the validity of the assumptions associated with the linear viscoelasticity. The measured time-temperature dependent properties are further verified by a shadow moiré and Twyman/Green test.

Evaluation of the conductor-like screening model for real solvents for the prediction of the water activity coefficient at infinite dilution in ionic liquids

Ionic liquids (ILs) have attracted great attention, from both industry and academia, as alternative fluids for very different types of applications. The large number of cations and anions allow a wide range of physical and chemical characteristics to be designed. However, the exhaustive measurement of all these systems is impractical, thus requiring the use of a predictive model for their study. In this work, the predictive capability of the conductor-like screening model for real solvents (COSMO-RS), a model based on unimolecular quantum chemistry calculations, was evaluated for the prediction water activity coefficient at infinite dilution, gamma(infinity)(w), in several classes of ILs. A critical evaluation of the experimental and predicted data using COSMO-RS was carried out. The global average relative deviation was found to be 27.2%, indicating that the model presents a satisfactory prediction ability to estimate gamma(infinity)(w) in a broad range of ILs. The results also showed that the basicity of the ILs anions plays an important role in their interaction with water, and it considerably determines the enthalpic behavior of the binary mixtures composed by Its and water. Concerning the cation effect, it is possible to state that generally gamma(infinity)(w) increases with the cation size, but it is shown that the cation-anion interaction strength is also important and is strongly correlated to the anion ability to interact with water. The results here reported are relevant in the understanding of ILs-water interactions and the impact of the various structural features of its on the gamma(infinity)(w) as these allow the development of guidelines for the choice of the most suitable lLs with enhanced interaction with water.

The scientific rationale, design and implementation plan for a Biogeochemical-Argo float array

Biogeochemical-Argo is the extension of the Argo array of profiling floats to include floats that are equipped with biogeochemical sensors for pH, oxygen, nitrate, chlorophyll, suspended particles, and downwelling irradiance. Argo is a highly regarded, international program that measures the changing ocean temperature (heat content) and salinity with profiling floats distributed throughout the ocean. Newly developed sensors now allow profiling floats to also observe biogeochemical properties with sufficient accuracy for climate studies. This extension of Argo will enable an observing system that can determine the seasonal to decadal-scale variability in biological productivity, the supply of essential plant nutrients from deep-waters to the sunlit surface layer, ocean acidification, hypoxia, and ocean uptake of CO2. Biogeochemical-Argo will drive a transformative shift in our ability to observe and predict the effects of climate change on ocean metabolism, carbon uptake, and living marine resource management. Presently, vast areas of the open ocean are sampled only once per decade or less, with sampling occurring mainly in summer. Our ability to detect changes in biogeochemical processes that may occur due to the warming and acidification driven by increasing atmospheric CO2, as well as by natural climate variability, is greatly hindered by this undersampling. In close synergy with satellite systems (which are effective at detecting global patterns for a few biogeochemical parameters, but only very close to the sea surface and in the absence of clouds), a global array of biogeochemical sensors would revolutionize our understanding of ocean carbon uptake, productivity, and deoxygenation. The array would reveal the biological, chemical, and physical events that control these processes. Such a system would enable a new generation of global ocean prediction systems in support of carbon cycling, acidification, hypoxia and harmful algal blooms studies, as well as the management of living marine resources. In order to prepare for a global Biogeochemical-Argo array, several prototype profiling float arrays have been developed at the regional scale by various countries and are now operating. Examples include regional arrays in the Southern Ocean (SOCCOM ), the North Atlantic Sub-polar Gyre (remOcean ), the Mediterranean Sea (NAOS ), the Kuroshio region of the North Pacific (INBOX ), and the Indian Ocean (IOBioArgo ). For example, the SOCCOM program is deploying 200 profiling floats with biogeochemical sensors throughout the Southern Ocean, including areas covered seasonally with ice. The resulting data, which are publically available in real time, are being linked with computer models to better understand the role of the Southern Ocean in influencing CO2 uptake, biological productivity, and nutrient supply to distant regions of the world ocean. The success of these regional projects has motivated a planning meeting to discuss the requirements for and applications of a global-scale Biogeochemical-Argo program. The meeting was held 11-13 January 2016 in Villefranche-sur-Mer, France with attendees from eight nations now deploying Argo floats with biogeochemical sensors present to discuss this topic. In preparation, computer simulations and a variety of analyses were conducted to assess the resources required for the transition to a global-scale array. Based on these analyses and simulations, it was concluded that an array of about 1000 biogeochemical profiling floats would provide the needed resolution to greatly improve our understanding of biogeochemical processes and to enable significant improvement in ecosystem models. With an endurance of four years for a Biogeochemical-Argo float, this system would require the procurement and deployment of 250 new floats per year to maintain a 1000 float array. The lifetime cost for a Biogeochemical-Argo float, including capital expense, calibration, data management, and data transmission, is about $100,000. A global Biogeochemical-Argo system would thus cost about $25,000,000 annually. In the present Argo paradigm, the US provides half of the profiling floats in the array, while the EU, Austral/Asia, and Canada share most the remaining half. If this approach is adopted, the US cost for the Biogeochemical-Argo system would be ~$12,500,000 annually and ~$6,250,000 each for the EU, and Austral/Asia and Canada. This includes no direct costs for ship time and presumes that float deployments can be carried out from future research cruises of opportunity, including, for example, the international GO-SHIP program (http://www.go-ship.org). The full-scale implementation of a global Biogeochemical-Argo system with 1000 floats is feasible within a decade. The successful, ongoing pilot projects have provided the foundation and start for such a system.

Changes in physical and chemical parameters of the traditional portuguese product água-mel during the production process

Changes in physical and chemical parameters (viscosity, total soluble solids and Hunter color parameters L*, a*, b*, chroma and hue angle) of água-mel were investigated throughout processing. Kinetic parameters for color change of heatprocessed água-mel were monitored. A zero-order kinetic model was applied to changes in L* and b*, while a* and C* were described using a first-order kinetic model. The heating process changed all three color parameters (L*, a*, b*), causing a shift toward the darker colors. Parameters L* decreased, while a*, b*, C* and hue angle (°h) increased during heating. Regarding changes in total soluble solids and in apparent viscosity, both fitted first-order kinetics. A direct relationship was found between the changes in these two parameters. The increase in both total soluble solids and viscosity affected a*, b* and C*. In addition, a flow diagram for the Portuguese água-mel production process has been established.

COMPUTATIONAL PREDICTION OF THE SPORULATION NETWORK IN CLOSTRIDIUM THERMOCELLUM

Sporulation is a process in which some bacteria divide asymmetrically to form tough protective endospores, which help them to survive in a hazardous environment for a quite long time. The factors which can trigger this process are diverse. Heat, radiation, chemicals and lacking of nutrition can all lead to the formation of endospores. This phenomenon will lead to low productivity during industrial production. However, the sporulation mechanism in a spore-forming bacterium, Clostridium theromcellum, is still unclear. Therefore, if a regulation network of sporulation can be built, we may figure out ways to inhibit this process. In this study, a computational method is applied to predict the sporulation network in Clostridium theromcellum. A working sporulation network model with 40 new predicted genes and 4 function groups is built by using a network construction program, CINPER. 5 sets of microarray expression data in Clostridium theromcellum under different conditions have been collected. The analysis shows the predicted result is reasonable.

Environmental assessment of industrial chemical and primary sector processes from a life cycle perspective

During the PhD program in chemistry at the University of Bologna, the environmental sustainability of some industrial processes was studied through the application of the LCA methodology. The efforts were focused on the study of processes under development, in order to assess their environmental impacts to guide their transfer on an industrial scale. Processes that could meet the principles of Green Chemistry have been selected and their environmental benefits have been evaluated through a holistic approach. The use of renewable sources was assessed through the study of terephthalic acid production from biomass (which showed that only the use of waste can provide an environmental benefit) and a new process for biogas upgrading (whose potential is to act as a carbon capture technology). Furthermore, the basis for the development of a new methodology for the prediction of the environmental impact of ionic liquids has been laid. It has already shown good qualities in identifying impact trends, but further research on it is needed to obtain a more reliable and usable model. In the context of sustainable development that will not only be sector-specific, the environmental performance of some processes linked to the primary production sector has also been evaluated. The impacts of some organic farming practices in the wine production were analysed, the use of the Cereal Unit parameter was proposed as a functional unit for the comparison of different crop rotations, and the carbon footprint of school canteen meals was calculated. The results of the analyses confirm that sustainability in the industrial production sector should be assessed from a life cycle perspective, in order to consider all the flows involved during the different phases. In particular, it is necessary that environmental assessments adopt a cradle-to-gate approach, to avoid shifting the environmental burden from one phase to another.

Applications of network-based approaches in drug research

Recent research trends in computer-aided drug design have shown an increasing interest towards the implementation of advanced approaches able to deal with large amount of data. This demand arose from the awareness of the complexity of biological systems and from the availability of data provided by high-throughput technologies. As a consequence, drug research has embraced this paradigm shift exploiting approaches such as that based on networks. Indeed, the process of drug discovery can benefit from the implementation of network-based methods at different steps from target identification to drug repurposing. From this broad range of opportunities, this thesis is focused on three main topics: (i) chemical space networks (CSNs), which are designed to represent and characterize bioactive compound data sets; (ii) drug-target interactions (DTIs) prediction through a network-based algorithm that predicts missing links; (iii) COVID-19 drug research which was explored implementing COVIDrugNet, a network-based tool for COVID-19 related drugs. The main highlight emerged from this thesis is that network-based approaches can be considered useful methodologies to tackle different issues in drug research. In detail, CSNs are valuable coordinate-free, graphically accessible representations of structure-activity relationships of bioactive compounds data sets especially for medium-large libraries of molecules. DTIs prediction through the random walk with restart algorithm on heterogeneous networks can be a helpful method for target identification. COVIDrugNet is an example of the usefulness of network-based approaches for studying drugs related to a specific condition, i.e., COVID-19, and the same ‘systems-based’ approaches can be used for other diseases. To conclude, network-based tools are proving to be suitable in many applications in drug research and provide the opportunity to model and analyze diverse drug-related data sets, even large ones, also integrating different multi-domain information.

Anticancer drug discovery using artificial intelligence: an application in pharmacological activity prediction

Hematological cancers are a heterogeneous family of diseases that can be divided into leukemias, lymphomas, and myelomas, often called “liquid tumors”. Since they cannot be surgically removable, chemotherapy represents the mainstay of their treatment. However, it still faces several challenges like drug resistance and low response rate, and the need for new anticancer agents is compelling. The drug discovery process is long-term, costly, and prone to high failure rates. With the rapid expansion of biological and chemical "big data", some computational techniques such as machine learning tools have been increasingly employed to speed up and economize the whole process. Machine learning algorithms can create complex models with the aim to determine the biological activity of compounds against several targets, based on their chemical properties. These models are defined as multi-target Quantitative Structure-Activity Relationship (mt-QSAR) and can be used to virtually screen small and large chemical libraries for the identification of new molecules with anticancer activity. The aim of my Ph.D. project was to employ machine learning techniques to build an mt-QSAR classification model for the prediction of cytotoxic drugs simultaneously active against 43 hematological cancer cell lines. For this purpose, first, I constructed a large and diversified dataset of molecules extracted from the ChEMBL database. Then, I compared the performance of different ML classification algorithms, until Random Forest was identified as the one returning the best predictions. Finally, I used different approaches to maximize the performance of the model, which achieved an accuracy of 88% by correctly classifying 93% of inactive molecules and 72% of active molecules in a validation set. This model was further applied to the virtual screening of a small dataset of molecules tested in our laboratory, where it showed 100% accuracy in correctly classifying all molecules. This result is confirmed by our previous in vitro experiments.