Data-driven and physics informed modeling of Chinese Hamster Ovary cell bioreactors
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Here, this paper studies the pathways to formulate lightweight and ultra-lightweight 3D printable cementitious composites. A hybrid approach was proposed by combining the advantages of traditional chemical-induced foaming (foaming approach) and lightweight particulate inclusions (synthetic foam approach). A comprehensive experimental program was conducted to evaluate the effects of foaming agents and fly ash cenosphere (FAC) on the printability, microstructure, mechanical and thermal properties of 3D printed samples. The results showed that the hybrid approach could produce a mixture with a density as low as 470 kg/m 3 while ensuring good flowability and buildability owing to the lubricating effect of foaming and supporting skeleton formed by FAC. In addition, a three-step homogenization procedure was also developed to predict the effective elastic modulus and thermal conductivity of 3D printable cementitious composites and cementitious foam. The findings of the study highlighted the effectiveness of the hybrid approach in formulating 3D printable ultra-lightweight cementitious composites in thermal insulation and acoustic applications.
Here, as photoautotrophic organisms, cyanobacteria capture and store solar energy in the form of biomass. Cyanobacterial biomass has been an important component of diet and nutrition in several regions for centuries. Synthetic biology strategies are currently being applied to increase the yield and productivity of cyanobacterial biomass by optimizing solar energy utilization and CO 2 fixation rates for carbon storage. Likewise, engineering cyanobacteria as cellular factories to synthesize carbohydrates, amino acids, proteins, lipids and fatty acids is providing an attractive way to sustainably produce food and nutrients for human consumption. In this review, we have summarized recent progress in both aspects and prospective trends under development.
Kinetic formalisms of metabolism link metabolic fluxes to enzyme levels, metabolite concentrations and their allosteric regulatory interactions. Though they require the identification of physiologically relevant values for numerous parameters, kinetic formalisms uniquely establish a mechanistic link across heterogeneous omics datasets and provide an overarching vantage point to effectively inform metabolic engineering strategies. Advances in computational power, gene annotation coverage, and formalism standardization have led to significant progress over the past few years. However, careful interpretation of model predictions, limited metabolic flux datasets, and assessment of parameter sensitivity remain as challenges. In this study we highlight fundamental considerations which influence model quality and prediction, advances in methodologies, and success stories of deploying kinetic models to guide metabolic engineering.
Pilgrim is a program written in Python and designed to use direct dynamics in the calculation of thermal rate constants of chemical reactions by the variational transition state theory (VTST), based on electronic structure calculations for the potential energy surface. Pilgrim can also simulate reaction mechanisms using kinetic Monte Carlo (KMC). For reaction processes with many elementary steps, the rate constant of each of these steps can be calculated by means of conventional transition state theory (TST) or by using VTST. In the current version, Pilgrim can evaluate thermal rates using the canonical version of reaction-path VTST, which requires the calculation of the minimum energy path (MEP) associated with each elementary step or transition structure. Multi-dimensional quantum effects can be incorporated through the small-curvature tunneling (SCT) approximation. These methodologies are available both for reactions involving a single structure of the reactants and the transition state and also for reactions involving flexible molecules with multiple conformations of the reactant and/or of the transition state. For systems with many conformers, the program can evaluate each of the elementary reaction rate constants by multipath canonical VTST or multi-structural VTST. Moreover, the reactant can be unimolecular or bimolecular. Torsional anharmonicity can be incorporated through either the MSTor or the Q2DTor programs. Dual-level calculations are also available in Pilgrim: automatic high-level single-point energies can be used to correct the energy of reactants, transition states, products, and MEP points using the interpolated single-point energies (ISPE) algorithm. When the rate constants of all the chemical processes of interest are known, by means of their calculation using Pilgrim or alternatively through analytical fits to the rate constants as functions of temperature, it is possible to simulate a multistep mechanism under specified laboratory conditions using KMC. Finally, this algorithm allows performing a kinetic simulation to monitor the evolution of each chemical species with time and obtain the product yields.
Characterizing the functional properties of plant acyl-ACP thioesterases (TEs), a key enzyme class used in the production of renewable oleochemicals in microbial hosts, experimentally, can be an expensive and time consuming process since it requires manual screening of thousands of candidates in a database. Using amino acid sequence to computationally predict an enzyme’s function might accelerate this process; however obtaining the necessary amount of information on previously characterized enzymes and their respective sequences required by standard Machine Learning (ML) based approaches to accurately infer sequence-function relationships can be prohibitive, especially with a low-throughput testing cycle. Experimental noise, unbalanced dataset where high sequence similarity does not always imply identical functional properties will further prevent robust prediction performance. Herein we present a ML method, Ensemble method for enZyme Classification (EnZymClass), that is specifically designed to address these issues. We used EnZymClass to classify TEs into short, long and mixed free fatty acid substrate specificity categories. While general guidelines for inferring substrate specificity have been proposed before, prediction of chain-length preference from primary sequence has remained elusive for plant acyl-ACP TEs. By applying EnZymClass to a subset of TEs in the ThYme database, we identified two medium chain TEs, ClFatB3 and CwFatB2, with previously uncharacterized activity in E. coli fatty acid production hosts.
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First-principles derived computational fluid dynamics (CFD) simulations have been proposed as a fundamental tool for investigating solvent-based CO 2 absorption in packed columns due to their ability to accurately represent the underlying nonlinear, multiscale dynamics. Numerous studies have previously utilized such CFD simulations to investigate hydrodynamics of columns with structured and random packings by assessing the key hydrodynamic metrics such as the interfacial and wetted areas. While mapping such metrics for different conditions is essential to the optimization of absorption columns, it is not sufficient, as the CO 2 capture rate depends also on the coupled, nonlinear dynamics from the underlying chemical reaction kinetics, thermodynamics, and heat-transfer rates. In this work, we present detailed CFD simulation results augmented by incorporating the effects of interfacial physical mass transfer of CO 2 , heat release from chemical reaction kinetics, and thermophysical property variations from resulting temperature gradients. We demonstrate the applicability of the proposed approach in numerically assessing the performance of packed columns by evaluating key hydrodynamic quantities, CO 2 absorption rates, and temperature rise in a reference column with packings that are structurally similar to the Sulzer Mellapak™ 250.Y packing, for different solvent inflow velocities and temperatures. Predictions from simulation results are found to be consistent with the trends in experimental observations from the literature, suggesting that the predictive capabilities of the simulation framework can be leveraged to guide the future development of absorber-column designs and optimized process flowsheets.
Soiling, particulate accumulation on photovoltaic (PV) module surfaces, reduces the available solar resource and the resulting generated device power. This case-study summarizes initial results of 5-year research on the contrasting soiling conditions in the tropical, subtropical, and semi-arid climates in Brazil. A major objective is to present a case study of the effects of soiling on PV module performance in different Brazil climate zones that represent the primary areas for the current and expanding Brazil solar installations. For this, the paper presents methodologies to quantify the soiling ratio (SRatio) and soiling rate (SRate) for two representative commercial technologies, polycrystalline or multicrystalline silicon (mc-Si) and thin-film cadmium telluride (CdTe) modules, through soiling monitoring stations deployed in the selected climate regions. An aim is to add to the growing soiling-research knowledge base through addressing these key factors and their relationships to critical electrical, solar resource, thermal, and local meteorological and environmental parameters. This paper presents, evaluates, and compares soiling rates and losses in Belo Horizonte, Minas Gerais (Equatorial Tropical: 19.92° S, 43.99° W), Porto Alegre, Rio Grande do Sul (Humid-Subtropical: 30.05° S, 51.17° W), and Brotas de Macaúbas, Bahia (Semi-Arid: 12.00° S, 42.63° W). The results show that soiling is moderate in all 3-regions, for example with 0.1%/day < SRate < 0.2%/day for Belo Horizonte. Precipitation dominates the cleaning of the modules in the summertime in this climate zone, while it is the major factor year-round in Rio Grande do Sul. Wind is the major issue mitigating the soiling accumulation for the Bahia installation. The methodology incorporates several key refinements, including the normalization and adjustment for the meteorological parameters (temperature, irradiance, wind, precipitation). The evaluations include the region-specific differing effects of non-uniform soiling, natural cleaning, and ambient temperatures.
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In this work, we report a facile approach for synthesizing M–N–C catalysts (M = Co, Fe, Ni) at a commercial scale without employing organic solvents. Our characterization efforts indicate that single atomic catalysts with high surface areas were successfully obtained. Electrochemical measurements demonstrate that, among the three synthesized catalysts, Ni–N–C exhibits the highest performance in the electrochemical CO2 reduction reaction (CO 2 RR) to carbon monoxide (CO), affording 80% Faradaic efficiency (FE) of CO production at –0.49 V RHE with a turnover frequency (TOF) of 57,379 h –1 . Large-scale synthesis coupled with high performance allows moving forward with the practical implementation of M–N–C catalysts for industrially relevant CO 2 RR.
Building-integrated carbon capture technology has the potential to reduce the cost of CO 2 capture while improving indoor air quality (IAQ). To promote the adoption of CO 2 capture in a building environment, this study investigated the possibility of integrating carbon capture technology with an existing rooftop make-up air unit (MAU) system to trap CO 2 . Here, in this study, a modular compact CO 2 capture system containing amine-functionalized polymer fibers was examined. The system, which was installed at the exhaust of the MAU, captures CO 2 before it leaves the building to enter the atmosphere as a greenhouse gas. The demonstrated average amount of CO 2 captured was 1.1–1.4 mmol/g of adsorbent material. Techno-economic analysis (TEA) was further performed on the CO 2 capture system, considering material costs, energy costs, as well as transportation and regeneration costs. These results were then used to estimate the levelized cost per ton CO 2 captured (LCOC). To achieve LCOC below $\$$100/t-CO 2 , adsorbents should have working capacities of 4.9 t and 3 t-CO 2 /year for 5 years and 10 years of operation, respectively. In summary, this study highlights a viable path toward the decarbonization of the commercial buildings sector and provides quantitative performance and economic insight on the suitability of building-integrated carbon capture technology.
We report that Multi-Agent Systems (MAS) are seen from different areas as one of the paramount trends for the next generation of power systems. Numerous published studies about MAS discuss its utilization in power distribution networks but none focuses on the prior step to restoration and self-healing that is fault section estimation. This paper aims to show how MAS can improve the utilities’ reliability indexes and consumer satisfaction by overcoming the multiple fault section estimation problem. In order to do this, the authors considered using MAS as a means of communication between smart meters. The purpose of smart meters usage is to employ devices that are already present in smart grids, mainly because of their reading and saving data capacity. The proposed method was tested on a radial feeder generated by the authors. The network was built on HYPERSIM, a software platform of OPAL-RT Technologies. The simulation results show that this MAS provides speed, efficiency, and automation for the process of fault section estimation.
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Here, the present study explores a hybrid thermal management technology based on air cooling and hybrid composite phase change material (HcPCM) filled finned heat sink for cooling performance of lower to medium heat flux dissipating electronic devices. Two-dimensional numerical simulations are conducted to study the conjugate heat transfer effects of three types of finned heat sink: air-cooled finned heat sink, HcPCM-cooled finned heat sink, and hybrid (air-HcPCM) cooled finned heat sink. A heat sink with a constant volume faction of plate-fins is designed in all cases and simultaneous effects of hybrid nanoparticles and air are investigated to keep the heat sink temperature at safe operating conditions between 40–60°C. The effect of air is incorporated into the heat sink by applying the convective heat transfer coefficient of h c = 10–100W /m 2 .K which tends to create the natural convection and forced convection heat transfer characteristics. The heat flux is varied from 25–40kW /m 2 in the current study. The hybrid nanoparticles of carbon additives (GO and MWCNTs) are dispersed into the RT-35HC, used as a PCM, with a volume fraction of 0% to 6%. Transient simulations are carried out using COMSOL Multiphysics to solve the governing equations for PCM based conjugate heat transfer model. The results showed that forced convection heat transfer improved the cooling performance of the hybrid heat sink compared to natural convection heat transfer. The addition of nanoparticles further enhanced thermal enhancement and uniform melting distribution of PCM inside the finned heat sink. The h c between 30 to 50W/m 2 .K shows optimized values for forced convection heat transfer operating conditions. The volume fraction of 2% of GO+MWCNTs nanoparticles in recommended or optimum concentration for uniform melting of PCM inside the finned heat sink.