cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/25607808
title: Model-based design of experiments and measurement optimization frameworks based on scalable and tractable algorithms and software

description: Mathematical models have become increasingly critical due to the rapid advances in computational methods in recent decades. 
However, the validation of these models often demands extensive and costly data, leading to time-consuming processes. 
Traditional design of experiments (DoE) methods struggle to choose informative experiments, especially for the typically large-scale, nonlinear, and dynamical science-based models in chemical and biomolecular engineering (CBE).
In this dissertation, I propose a sequential model validation workflow powered by novel DoE and measurement optimization (MO) frameworks to improve data acquisition efficiency and accelerate the model building and validation process. The workflow relies on two scalable and tractable frameworks, along with their generalized open-source software tools: 

 Measurement optimization: determines what to measure for experiments to maximize the experimental information content. It guides apparatus preparation during the experimental setup stage, balancing the information content with practical constraints such as budgets 
 Model-based design of experiments: quantifies experimental information content statistically and optimizes experiment selection based on updated model information. This framework is used throughout the model validation process, recommending new experiments after each iteration to update the model

Both frameworks focus on addressing the challenges of DoE and MO techniques leveraging large-scale, nonlinear, and dynamical models in CBE, providing user-friendly open-source software tools for widespread applications.


In this dissertation, I describe the development of the model-based DoE and the MO frameworks and their generalized tools to streamline the model validation workflow for complex models such as partial differential algebraic equations (PDAEs). I briefly discuss how these frameworks and the open-source software tool contribute to the broad DoE technique paradigm and applications. I demonstrate the tractability and scalability of the frameworks with laboratory and pilot-scale carbon capture experiments. Moreover, generalized open-source software tools are developed and applied to carbon capture experiments, highlighting their versatility and practicality. This dissertation lays the groundwork for a sequential MO and MBDoE workflow that can be readily applied to various challenging problems in CBE and beyond, offering potential benefits to broader science, technology, engineering, and math (STEM) communities. I conclude with a discussion of the future directions, and provide preliminary works for some future directions as a starting point.
DOI: 10.7274/25607808.v1
title: Model-based design of experiments and measurement optimization frameworks based on scalable and tractable algorithms and software

description: Mathematical models have become increasingly critical due to the rapid advances in computational methods in recent decades. 
However, the validation of these models often demands extensive and costly data, leading to time-consuming processes. 
Traditional design of experiments (DoE) methods struggle to choose informative experiments, especially for the typically large-scale, nonlinear, and dynamical science-based models in chemical and biomolecular engineering (CBE).
In this dissertation, I propose a sequential model validation workflow powered by novel DoE and measurement optimization (MO) frameworks to improve data acquisition efficiency and accelerate the model building and validation process. The workflow relies on two scalable and tractable frameworks, along with their generalized open-source software tools: 

 Measurement optimization: determines what to measure for experiments to maximize the experimental information content. It guides apparatus preparation during the experimental setup stage, balancing the information content with practical constraints such as budgets 
 Model-based design of experiments: quantifies experimental information content statistically and optimizes experiment selection based on updated model information. This framework is used throughout the model validation process, recommending new experiments after each iteration to update the model

Both frameworks focus on addressing the challenges of DoE and MO techniques leveraging large-scale, nonlinear, and dynamical models in CBE, providing user-friendly open-source software tools for widespread applications.


In this dissertation, I describe the development of the model-based DoE and the MO frameworks and their generalized tools to streamline the model validation workflow for complex models such as partial differential algebraic equations (PDAEs). I briefly discuss how these frameworks and the open-source software tool contribute to the broad DoE technique paradigm and applications. I demonstrate the tractability and scalability of the frameworks with laboratory and pilot-scale carbon capture experiments. Moreover, generalized open-source software tools are developed and applied to carbon capture experiments, highlighting their versatility and practicality. This dissertation lays the groundwork for a sequential MO and MBDoE workflow that can be readily applied to various challenging problems in CBE and beyond, offering potential benefits to broader science, technology, engineering, and math (STEM) communities. I conclude with a discussion of the future directions, and provide preliminary works for some future directions as a starting point.
DOI: 10.7274/26007067
title: Affinity Membranes Enable Rapid Quantitation of Therapeutic Antibodies and Analysis of Glycosylation

description: This research develops peptide-functionalized porous membranes to capture therapeutic monoclonal antibodies (mAbs) for rapid analysis of their concentrations and N-glycosylation patterns. Established approaches for determining these mAb critical quality attributes are typically complex and time consuming. Techniques that rapidly analyze mAb concentrations and glycan profiles will enable monitoring of these attributes during manufacturing and development.
To quantify mAb concentrations in minutes, this work employs membrane-containing 96-well plates that capture most classes of mAbs from fermentation broths. Flow-induced mass transport in membrane pores enables immobilized peptide ligands to rapidly bind to the fragment crystallizable (Fc) region of mAbs. Subsequent passage of a fluorophore-labelled secondary antibody generates a fluorescence signal, which is proportional to the target mAb concentration. Because the Fc regions of immunoglobulin G (IgG) antibodies are very similar, this method is effective for various IgG subclasses. The assay gives the accuracy, precision, and limit of detection (LOD) needed for analysis of mAbs in expression systems and requires &lt; 5 min. Thus, membrane-based analyses could aid in quality control and early intervention during mAb production.
Analysis of mAb glycans relies on a workflow that includes enriching mAbs from cell culture supernatant, eluting and digesting the captured mAbs, and analyzing their glycan patterns using liquid chromatography – tandem mass spectrometry (LC-MS/MS). The process uses in-membrane pepsin digestion that is fast (&lt; 5 min) and does not require alkylation and desalting, greatly simplifying the workflow. This strategy requires &lt; 2 h and could help to monitor the continuity of mAb glycosylation during fermentation. Demonstrations of the technique include examination of glycosylation in different batches and subclasses of mAbs, in biosimilars, and in mAbs from different expression systems. Overall, membrane-based devices show great promise for rapid analyses of therapeutic mAbs.
DOI: 10.7274/26007067.v1
title: Affinity Membranes Enable Rapid Quantitation of Therapeutic Antibodies and Analysis of Glycosylation

description: This research develops peptide-functionalized porous membranes to capture therapeutic monoclonal antibodies (mAbs) for rapid analysis of their concentrations and N-glycosylation patterns. Established approaches for determining these mAb critical quality attributes are typically complex and time consuming. Techniques that rapidly analyze mAb concentrations and glycan profiles will enable monitoring of these attributes during manufacturing and development.
To quantify mAb concentrations in minutes, this work employs membrane-containing 96-well plates that capture most classes of mAbs from fermentation broths. Flow-induced mass transport in membrane pores enables immobilized peptide ligands to rapidly bind to the fragment crystallizable (Fc) region of mAbs. Subsequent passage of a fluorophore-labelled secondary antibody generates a fluorescence signal, which is proportional to the target mAb concentration. Because the Fc regions of immunoglobulin G (IgG) antibodies are very similar, this method is effective for various IgG subclasses. The assay gives the accuracy, precision, and limit of detection (LOD) needed for analysis of mAbs in expression systems and requires &lt; 5 min. Thus, membrane-based analyses could aid in quality control and early intervention during mAb production.
Analysis of mAb glycans relies on a workflow that includes enriching mAbs from cell culture supernatant, eluting and digesting the captured mAbs, and analyzing their glycan patterns using liquid chromatography – tandem mass spectrometry (LC-MS/MS). The process uses in-membrane pepsin digestion that is fast (&lt; 5 min) and does not require alkylation and desalting, greatly simplifying the workflow. This strategy requires &lt; 2 h and could help to monitor the continuity of mAb glycosylation during fermentation. Demonstrations of the technique include examination of glycosylation in different batches and subclasses of mAbs, in biosimilars, and in mAbs from different expression systems. Overall, membrane-based devices show great promise for rapid analyses of therapeutic mAbs.
DOI: 10.7274/26299258
title: Engineering Peptide Self-Assembly for Glucose-Responsive Glucagon Delivery

description: Stimuli-responsive materials are gaining traction in precise drug delivery, especially for diabetes management. Intensified insulin treatment can cause acute hypoglycemia (BGL &lt; 70 mg/dL), leading to severe complications. Over 3 million severe hypoglycemic emergencies occur annually in the US. Administering exogenous glucagon during such episodes is critical, but impaired consciousness and nighttime occurrences hinder self-administration, particularly in children. Hence, an autonomous, smart glucagon delivery system is urgently needed.
Stimuli-responsive materials enhance therapeutic efficacy by responding to analytes like pH, enzymes, glucose, and redox agents. Peptide self-assemblies are particularly effective due to their natural ability to form well-defined nanostructures and respond to specific stimuli for targeted drug delivery.
Chapter 1 introduces disease-analyte-responsive peptide self-assemblies, focusing on design strategies for peptides that respond to cancer, inflammation, and diabetes-related analytes. It discusses leveraging peptide properties like hydrophobicity and secondary structure, emphasizing glucose-responsive materials.
My research developed materials inspired by biological systems that exist in a non-equilibrium state, influenced by energy sources like ATP or GTP. Chapter 2 explores glucose oxidase (GOx) as a sensor to convert glucose signals into pH stimuli, driving pH-responsive peptide self-assembly into a hydrogel. This hydrogel encapsulates glucagon at normal glucose levels and releases it when glucose drops, marking a new paradigm in glucose-responsive material design. Despite challenges like GOx instability and cytotoxicity, this strategy showed promise.
Chapters 3 and 4 investigate phenylboronic acid (PBA) as an alternative sensor. PBA forms stable boronated esters with glucose's cis-diols, acting as a switch in materials. Chapter 3 details a PBA-multidomain peptide (PBA-MDP) that forms a self-supporting hydrogel with rapid glucose-triggered responses, improving efficacy in mice. Chapter 4 explores peptide-based droplets, integrating PA-PBA with dasiglucagon for glucose-driven liquid-liquid phase separation (LLPS). These stabilized droplets proved effective in alleviating severe hypoglycemia in a mice model, offering a novel approach to glucagon delivery.
DOI: 10.7274/26299258.v1
title: Engineering Peptide Self-Assembly for Glucose-Responsive Glucagon Delivery

description: Stimuli-responsive materials are gaining traction in precise drug delivery, especially for diabetes management. Intensified insulin treatment can cause acute hypoglycemia (BGL &lt; 70 mg/dL), leading to severe complications. Over 3 million severe hypoglycemic emergencies occur annually in the US. Administering exogenous glucagon during such episodes is critical, but impaired consciousness and nighttime occurrences hinder self-administration, particularly in children. Hence, an autonomous, smart glucagon delivery system is urgently needed.
Stimuli-responsive materials enhance therapeutic efficacy by responding to analytes like pH, enzymes, glucose, and redox agents. Peptide self-assemblies are particularly effective due to their natural ability to form well-defined nanostructures and respond to specific stimuli for targeted drug delivery.
Chapter 1 introduces disease-analyte-responsive peptide self-assemblies, focusing on design strategies for peptides that respond to cancer, inflammation, and diabetes-related analytes. It discusses leveraging peptide properties like hydrophobicity and secondary structure, emphasizing glucose-responsive materials.
My research developed materials inspired by biological systems that exist in a non-equilibrium state, influenced by energy sources like ATP or GTP. Chapter 2 explores glucose oxidase (GOx) as a sensor to convert glucose signals into pH stimuli, driving pH-responsive peptide self-assembly into a hydrogel. This hydrogel encapsulates glucagon at normal glucose levels and releases it when glucose drops, marking a new paradigm in glucose-responsive material design. Despite challenges like GOx instability and cytotoxicity, this strategy showed promise.
Chapters 3 and 4 investigate phenylboronic acid (PBA) as an alternative sensor. PBA forms stable boronated esters with glucose's cis-diols, acting as a switch in materials. Chapter 3 details a PBA-multidomain peptide (PBA-MDP) that forms a self-supporting hydrogel with rapid glucose-triggered responses, improving efficacy in mice. Chapter 4 explores peptide-based droplets, integrating PA-PBA with dasiglucagon for glucose-driven liquid-liquid phase separation (LLPS). These stabilized droplets proved effective in alleviating severe hypoglycemia in a mice model, offering a novel approach to glucagon delivery.
DOI: 10.7274/26301121
title: Molecular-to-Systems Scale Design of Diafiltration Cascades for Improved Solute Recovery and Purity

description: Sustainable energy solutions and electrification are driving increased demand for critical minerals; albeit the existing technologies are at odds with the sustainability goals driving increased demand for critical minerals. The small footprint and modular nature of membrane technologies position them well to substitute current technologies as they are able to address the declining concentrations in ores and brines, the variable feed concentrations encountered in recycling, and the environmental issues associated with current separation processes. The success of implementing membranes into these processes’ hinges on the exploration and rapid development of new and existing materials over industrially diverse feed conditions.
In this context, this dissertation establishes new characterization techniques, which address knowledge gaps related to the interfacial processes that govern solute-solute selectivity and the performance of membranes in complex multi-component feeds streams, to advance membrane processes. Guided by the tools of data science, a diafiltration apparatus is developed to inform material and process design by rapidly characterizing membrane performance over a broad range of feed solution compositions. The apparatus is built from the ground up and validated with commercial membranes. One key component of the apparatus is the automation, implemented throughout the data collection and processing, that will become a critical piece towards developing self-driving laboratories. This dissertation subsequently extends the utility of the diafiltration apparatus to systematically characterize the effect of complex feed compositions on solute transport. The characterization of solute transport coupled with the systems scale design of diafiltration cascades highlights opportunities in which membranes can transform the field of separations.
DOI: 10.7274/26301121.v1
title: Molecular-to-Systems Scale Design of Diafiltration Cascades for Improved Solute Recovery and Purity

description: Sustainable energy solutions and electrification are driving increased demand for critical minerals; albeit the existing technologies are at odds with the sustainability goals driving increased demand for critical minerals. The small footprint and modular nature of membrane technologies position them well to substitute current technologies as they are able to address the declining concentrations in ores and brines, the variable feed concentrations encountered in recycling, and the environmental issues associated with current separation processes. The success of implementing membranes into these processes’ hinges on the exploration and rapid development of new and existing materials over industrially diverse feed conditions.
In this context, this dissertation establishes new characterization techniques, which address knowledge gaps related to the interfacial processes that govern solute-solute selectivity and the performance of membranes in complex multi-component feeds streams, to advance membrane processes. Guided by the tools of data science, a diafiltration apparatus is developed to inform material and process design by rapidly characterizing membrane performance over a broad range of feed solution compositions. The apparatus is built from the ground up and validated with commercial membranes. One key component of the apparatus is the automation, implemented throughout the data collection and processing, that will become a critical piece towards developing self-driving laboratories. This dissertation subsequently extends the utility of the diafiltration apparatus to systematically characterize the effect of complex feed compositions on solute transport. The characterization of solute transport coupled with the systems scale design of diafiltration cascades highlights opportunities in which membranes can transform the field of separations.
DOI: 10.7274/26933167
title: Computational Interrogation of Catalytic Surface Speciation Under Highly Reactive Environments

description: The catalyst is the heart of a reaction process that transforms an original substance into a usable product. To rationally design practical catalysts with high selectivity and reactivity, it is necessary to understand the fundamental mechanisms behind these transformations. In this dissertation, I will describe my research project, focusing on the catalytic surface reactions involving N2 and CO molecules. The Density Functional Theory (DFT) approach is utilized to carefully examine the binding energies and vibrational characteristics of potential surface adsorbates across various transition metals. A comparison of nitrogen-containing species produced during N2 plasma exposure unravels the complexity of surface metastable speciation and the influence of trace CO molecules. A DFT-parameterized microkinetic model is employed for the plasma-assisted N2 oxidation reaction to study product selectivity and sensitivity, showing dependence on catalyst material and experimental temperature. Lastly, we explored the CO binding energies on Pd and Pd77Ag23 surfaces, revealing that CO inhibits H2 permeation on Pd and Pd77Ag23 foil membranes by occupying surface Pd atoms and subsequently influencing H2 dissociation.
DOI: 10.7274/26933167.v1
title: Computational Interrogation of Catalytic Surface Speciation Under Highly Reactive Environments

description: The catalyst is the heart of a reaction process that transforms an original substance into a usable product. To rationally design practical catalysts with high selectivity and reactivity, it is necessary to understand the fundamental mechanisms behind these transformations. In this dissertation, I will describe my research project, focusing on the catalytic surface reactions involving N2 and CO molecules. The Density Functional Theory (DFT) approach is utilized to carefully examine the binding energies and vibrational characteristics of potential surface adsorbates across various transition metals. A comparison of nitrogen-containing species produced during N2 plasma exposure unravels the complexity of surface metastable speciation and the influence of trace CO molecules. A DFT-parameterized microkinetic model is employed for the plasma-assisted N2 oxidation reaction to study product selectivity and sensitivity, showing dependence on catalyst material and experimental temperature. Lastly, we explored the CO binding energies on Pd and Pd77Ag23 surfaces, revealing that CO inhibits H2 permeation on Pd and Pd77Ag23 foil membranes by occupying surface Pd atoms and subsequently influencing H2 dissociation.
DOI: 10.15468/dl.a38vy2
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Colección Briológica del Herbario LIL (FML–Tucumán)",
 "Country is Croatia"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008012-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fgruf2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pelophylax esculentus (Linnaeus, 1758)",
 "Year is 44",
 "Country is Netherlands"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008013-241107131044228/datasets/export for details.
DOI: 10.15468/dl.a8naqq
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Dalmatosomatidae, a new monotypic family, and Dalmatosoma agaricum gen. et sp. nov. (Diplopoda: Chordeumatida: Craspedosomatidea) from Croatia, Balkan Peninsula",
 "Country is Croatia"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008014-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sbj7xm
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Database of field studies on environmental impacts of invasive plant species in Europe",
 "Country is Croatia"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008015-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.