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Accurate Free Energies of Aqueous Electrolyte Solutions from Molecular Simulations with Non-polarizable Force Fields
No full textNon-polarizable force fields fail to accurately predict free energies of aqueous electrolytes without compromising the predictive ability for densities and transport properties. A new approach is presented in which (1) TIP4P/2005 water and scaled charge force fields are used to describe the interactions in the liquid phase and (2) an additional Effective Charge Surface (ECS) is used to compute free energies at zero additional computational expense. The ECS is obtained using a single temperature-independent charge scaling parameter per species. Thereby, the chemical potential of water and the free energies of hydration of various aqueous salts (e.g., NaCl and LiCl) are accurately described (deviations less than 5% from experiments), in sharp contrast to calculations where the ECS is omitted (deviations larger than 20%). This approach enables accurate predictions of free energies of aqueous electrolyte solutions using non-polarizable force fields, without compromising liquid-phase properties.Engineering ThermodynamicsTeam Poulumi De
Editorial: Modelling Values in Social, Technical, and Ecological Systems
No full textThis editorial paper for the special section on “Modelling Values in Socio/Technical/Ecological Systems” introduces interdisciplinary perspectives on values and reflects on growing appeals for modelling values. In public and academic discourses, values typically relate to matters of importance (e.g., beliefs, priorities) and principles about what is considered to be good (e.g., moral values) and are often seen as shaping individual and collective behaviour. As shown by eight contributions to this special section, it is relevant for social simulation modelling to dive deeper into embedding values in models in order to explore behavioural change on different levels and across contexts. Our goal with this special section is to stimulate interest in developing various approaches that study and operationalise values in agent-based models to investigate the complex problems raised in social, socio-technical and socio-ecological systems. We conclude with a call for future research to be explicit in their modelling assumptions, thus fostering a vigorous foundation for scientific discourse.Ethics & Philosophy of TechnologySystem Engineerin
On the effectiveness of Reynolds-averaged and subgrid scale models in predicting flows inside car cabins
No full textThe aim of the present study is to analyze the performances of unsteady Reynolds-averaged Navier-Stokes (URANS) and large eddy simulation (LES) approaches in predicting the airflow patterns inside car cabins and to give insight in the design of computational fluid dynamics simulations of a real car cabin. For this purpose, one eddy viscosity-based turbulence model (shear stress transport k-ω) and two subgrid scale models (wall-adapting local eddy-viscosity and dynamic kinetic energy) were tested, and numerical results were compared with particle image velocimetry measurements carried out on a commercial car. The URANS model exhibited great accuracy in predicting the mean flow behavior and was appreciably outperformed by the LES models only far from the inlet sections. For this reason, it was deemed suitable for conducting further analyses, aimed at characterizing the airflow patterns in winter and summer conditions and performing a thermal comfort analysis. The thermal regime was found to have a very little effect on the air flow patterns, once the quasi-steady state regime is achieved; in fact, both in winter and in summer, the temperature field is fairly uniform within the car cabin, making the contribution of buoyancy negligible and velocity fields to be very similar in the two seasons. Findings also reveal that thermal comfort sensation can be different for passengers sharing the same car but sitting on different seats; this aspect should be considered when designing and operating the ventilation system, since the minimum comfort requirements should be met for all the occupants.Aerodynamic
Reader-friendly Edible Binarycodes and Sensors Based on Smart Hydrogel
No full textFood and medicines are two of the most essential categories of goods for human beings, providing vital nourishment and healthcare. However, as these products are commercialized and distributed on a global scale, consumers face the threat of counterfeit and deteriorated products. In response, this dissertation presents four prototypes consisting of On-Dose-Authentication (ODA) binarycodes and battery-less indicators based on smart hydrogel that are edible and reader-friendly to address these issues. First, a microfluidic platform for continuous synthesis of hydrogel microparticles with superparamagnetic colloids (SPCs) embedded at prescribed positions has been established. The shape of the cross-linked microparticle is independently controlled by stop-flow lithography, whereas the position of trapped SPCs is dictated by virtual magnetic molds made of 2D nickel patches facilitating magnetic trapping. The spatial positions of trapped SPCs collectively function as a binary code matrix for product authentication. The proposed magnetic microparticles will contribute to the development of soft matter-inspired product quality control, tracking, and anti-counterfeiting technologies. (Chapter 2)Second, a Physical Unclonable Functions (PUF) algorithm was developed to enhance the ODA binary codes' safety level. This algorithm exploits the diameter and coordinates of spheres as input, abandoning color and intensity as inputs, enabling imaging using common illumination and low-magnification microscopy hence lifting the reading constraints to advanced labs that are usually found in other current graphical PUF systems. Two sets of Poly(ethylene glycol) diacrylate ODA-PUF tags that can be read out via this algorithm were fabricated. The sets are single-diameter PUF leveraging random distributed superparamagnetic colloids of identical diameters and multiple-diameter PUF utilizing vortexed sunflower oil drops of various diameters, respectively. The performance of the single-diameter system was investigated. It passed NIST Statistical tests, demonstrating sufficient randomness, ideal bit uniformity, Hamming distance, and device uniqueness. The encoding capacity of the single-diameter system was found to be , which can satisfy labeling the annual output of Aspirin. (Chapter 3) Third, a humidity indicator has been created that mechanically bends and rolls itself irreversibly upon exposure to high humidity conditions. The indicator is made of two food-grade polymer films with distinct ratios of a milk protein, casein, and a plasticizer, glycerol, that are physically attached to each other. Based on the thermogravimetric analysis and microstructural characterization, the bending mechanism is a result of hygroscopic swelling and consequent counter diffusion of water and glycerol. Guided by this mechanism, the rolling behavior, including response time and final curvature, can be tuned by the geometric dimensions of the indicator. As the proposed indicator is made of food-grade ingredients, it can be placed directly in contact with perishable products to report exposure to undesirable humidity inside the package, without the risk of contaminating the product or causing oral toxicity in case of accidental ingestion - features that commercial inedible electronic and chemo-chromatic sensors cannot provide presently. (Chapter 4)Finally, an alginate TTI bead that encapsulates betacyanin, a natural colorant extracted from purple pitaya, is proposed to continuously monitor and reflect the temperature history of the perishable products to diagnose the storage conditions. The instability of betacyanin is exploited to demonstrate undesirable temperature abuse through visual color changes. The thermochromic change of the purple pitaya extract and the pitaya-extract-encapsulated bead was investigated under various temperatures, pH, and gaseous atmosphere conditions. Experimental results show that the proposed TTI exhibits an irreversible thermochromic change under a wide operation temperature range up to at least 100 \textdegree C with negligible disturbance from the gaseous composition. (Chapter 5)Engineering Thermodynamic
Continuous Chromatography of Biopharmaceuticals: Next Generation Process Development
No full textThe biopharmaceutical industry is moving from a batch to a continuous mode of manufacturing. This shift promises to reduce costs and manufacturing footprint while improving productivity and consistency of the product. This thesis implements miniaturized and automated high-throughput screening techniques alongside a mathematical chromatography model for the development of an integrated continuous chromatography process. The model is used for in-silico optimization of a capture and polishing step of a monoclonal antibody (mAb). The optimization focusses on chromatographic processes that would have to deal with higher titer solutions.The transition to Integrated Continuous Biomanufacturing (ICB) is welcomed by industry and regulatory agencies, which are working together to accomplish this shift. Process development plays a crucial role in defining new processes or adapting existing processes to different modes of operation. High-Throughput Process Development (HTPD) has been used in the biopharmaceutical industry to accelerate and reduce costs of process development, by using miniaturized assays and performing computer-aided studies. However, the industry experiences gaps and sees opportunities for improvement in the HTPD tools that can help the transition to ICB. These gaps, together with a state-of-the-art of HTPD for ICB are presented in Chapter 2. Experts in the field identified microfluidics and modeling to be the most promising technologies to fill in the gaps in process development for ICB.Subsequently, an overview on the state-of-the-art of automation and miniaturization for biopharmaceutical process development is given in Chapter 3. The focus is on different degrees of miniaturization and automation of the technologies for process development, for both Upstream and Downstream processing (USP and DSP, respectively). Liquid-Handling Stations (LHS) are the epitome of automation for process development, and have seen great adoption for the past decades. Examples of the use of this tool for USP and DSP process development are provided. A greater emphasis is placed on the often overlooked microfluidics and how it can also be used as a screening tool, and a SWOT analysis on LHS and microfluidics as potential process development tools is provided.Further comparison between HTS tools for chromatographic process development is needed, since process development efforts for chromatography mostly rely on LHS-based experiments. Three methodologies are selected for this comparison: LHS, microfluidics, and Eppendorf tubes (Chapter 4). To achieve this, protein equilibrium adsorption isotherms are determined with each of the aforementioned methodologies. The microfluidics chip produced in-house provides a platform for resin screening that achieves liquid and resin volume reductions of 15- and up-to 200-fold, respectively. Accurate resin volume determination is ensured with an image analysis software, and resin consumption is as high as 200 nl in the microfluidics system. After validating the HTS methodologies, a cost consideration study aims at fairly comparing the three methodologies for their chromatographic process development potential. Although at a lower Technology Readiness Level, microfluidics can be a viable alternative tool when the protein to be studied is very expensive or scarce (such as in early stages of process development), due to the high degree of miniaturization. Furthermore, it is discussed what would be the possible applications of the different methodologies in chromatographic process development.The HTS methodologies developed paved the way for the implementation of a HTPD approach for the study and optimization of continuous chromatography (Chapters 5 and 6). A large database on the adsorption equilibrium isotherms of mAbs to different protein A (ProA) and Cation-Exchange (CEX) resins is generated from experiments with a LHS. This database is then used to further reduce resin candidates to be used in subsequent experiments. Four resin candidates are used to study the equilibrium adsorption isotherms of mAb to ProA ligands with a clarified cell culture supernatant (harvest). It is shown that pure mAb experiments reflect the same adsorption behavior as harvest experiments for all resin candidates, reducing the need to duplicate experiments in the future. The parameters determined are further used in a mechanistic Lumped Kinetic Model (LKM), used for the in-silico study of column chromatography (Chapter 5). The LKM uses a lumped overall mass transfer parameter that is linearly dependent on feed concentration, in line with mass transfer theory. The hybrid approach to HTPD emphasizes the importance of computational, experimental, and decision-making stages in chromatographic process development.The LKM model described is further developed for the study of continuous chromatography. The continuous model is used for the in-silico optimization of a 3-Column Periodic Counter-current Chromatography (3C-PCC) capture and polishing step, for the purification of mAbs from high-titer solutions (Chapter 6). The model maximizes Productivity and Capacity Utilization (CU) keeping the yield high (99%) and having the flow rate and the percentage of breakthrough achieved in the interconnected phase as constraints. The shape of the breakthrough curve plays an important role in the optimization of continuous chromatography. The optimization results are validated for three different ProA resins, from which the best resin candidate is selected to continuously capture mAb from a harvest solution. The eluates of this operation are pooled and used as input for the continuous CEX step. The experimental results show very good agreement with model’s predictions (lower than 7% deviation) and the proposed methodology helps to develop and optimize a continuous chromatography process in a short amount of time.In summary, this thesis presents the exciting journey of process development for continuous chromatography, from conceptualization and selection of screening techniques until the end result of performing an optimized continuous chromatographic step for the successful capture and polishing of a mAb.BT/Bioprocess Engineerin
Supercurrent mediated by helical edge modes in bilayer graphene
No full textBilayer graphene encapsulated in tungsten diselenide can host a weak topological phase with pairs of helical edge states. The electrical tunability of this phase makes it an ideal platform to investigate unique topological effects at zero magnetic field, such as topological superconductivity. Here we couple the helical edges of such a heterostructure to a superconductor. The inversion of the bulk gap accompanied by helical states near zero displacement field leads to the suppression of the critical current in a Josephson geometry. Using superconducting quantum interferometry we observe an even-odd effect in the Fraunhofer interference pattern within the inverted gap phase. We show theoretically that this effect is a direct consequence of the emergence of helical modes that connect the two edges of the sample. The absence of such an effect at high displacement field, as well as in bare bilayer graphene junctions, supports this interpretation and demonstrates the topological nature of the inverted gap.QRD/Goswami LabBUS/TNO STAF
The impact of third generation sequencing on haplotype assembly
No full textThe genome encompasses an organism’s full DNA, organized into chromosomes within the cell nucleus. Humans have 46 paired chromosomes, and within these pairs, genetic information is grouped as haplotypes—genetic packages passed from one generation to the next, ensuring genetic diversity. While DNA sequencing produces short fragments or reads, assembling these back into a complete genome can be complex. The presence of multiple, similar haplotypes in some organisms amplifies this complexity, emphasizing the need for specialized techniques to accurately capture these subtle genetic variations.In this thesis, we dive into the de novo and haplotype assembly challenges. We aimto tackle haplotype assembly challenges and find better ways to accurately assemble the genetic puzzle pieces. Along the way, we introduce a new tool for haplotype assembly designed to make the process more interpretable.Pattern Recognition and Bioinformatic
Green Bond Valuation: A Numerical Mathematics Perspective: Assessing the Influence of Environmental Factors
No full textThis thesis presents a novel approach to the pricing of green bonds, a growing segment in financial markets with an emphasis on environmental sustainability. Unlike traditional financial instruments, green bonds uniquely incorporate environmental considerations, particularly carbon price (c_t), along with traditional factors like the short rate (r_t), into their valuation. This integration is increasingly relevant in today’s economy, reflecting a shift towards sustainable finance. The core of this research involves applying advanced numerical methods, including the Finite Difference Method, Crank-Nicolson discretization, GMRES and Bi-CGSTAB, in order to develop and analyze pricing models for both green and conventional bonds. The study aims to assess how environmental factors impact the efficiency of these numerical techniques and to compare the outcomes with conventional bond models. The research reveals that green bonds, compared to conventional bonds, present unique numerical challenges, notably requiring more iterations for convergence in iterative methods GMRES and Bi-CGSTAB because of the high carbon price volatility (σc) and the ’Greenium’ phenomenon. Moreover, the comparative analysis showed that while Bi-CGSTAB outperforms GMRES in the green bond model, the opposite is true for conventional bonds. This study not only contributes to the theoretical understanding of green bond pricing but also offers practical insights for financial analysts and investors navigating this evolving market.Keywords: Green Bonds, Bond Pricing, Zero-Coupon Bond, Short Rate Modeling, Numerical Methods, Crank-Nicolson, GMRES, BiCGSTAB, Environmental Finance, Sustainable Investing, Comparative Analysis, Financial Modeling.Applied Mathematics | Financial Engineerin
Temporal dynamics of resilience in the palladium supply chain: A data analytics approach
No full textPalladium is a critical raw material that is considered economically and strategically important by various national governments. The palladium supply chain is subject to several supply chain risks, including the geopolitical risk of potential Russian palladium export restrictions. Considering these disruption risks, it is essential to gain insight into the resilience of the palladium supply chain. Accordingly, this study investigates the temporal dynamics of resilience in the palladium supply chain using literature review, data analysis, and regression modelling. To that end, the diversity of supply, stockpiling, price, and substitution mechanisms from the qualitative resilience framework by Sprecher et al. (2015) are operationalised in terms of quantitative proxy variables. A PCA-weighted compound resilience index is constructed and quantitatively validated using the palladium market balance as a resilience performance indicator. It is found that the palladium supply chain exhibited an overall improvement of resilience, but still a structural lack of resilience during the years 2012-2021. This thesis contributes to the scientific study of how material criticality and resilience change over time. Moreover, this study informs policy-makers about potential risks for the palladium supply chain and makes policy recommendations to improve the resilience of the palladium supply chain.https://drive.google.com/drive/folders/1wCprTfJGjo6QvgZBjBKBoIyDDmMUJCPG?usp=sharing Link to data filesEngineering and Policy Analysi
Label synchronization for Hybrid Federated Learning in manufacturing and predictive maintenance
No full textArtificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information. However, conventional FL methods struggle to handle situations where data samples have diverse features and sizes. We propose a Hybrid Federated Learning solution with label synchronization to overcome this challenge. Our FedLabSync algorithm trains a feed-forward Artificial Neural Network while alerts that it can aggregate knowledge of other ML architectures compatible with the Stochastic Gradient Descent algorithm by conducting a penalized collaborative optimization. We conducted two industrial case studies: product inspection in Bosch factories and aircraft component Remaining Useful Life predictions. Our experiments on decentralized data scenarios demonstrate that FedLabSync can produce a global AI model that achieves results on par with those of centralized learning methods.Air Transport & Operation
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