Showing papers by "Michigan Technological University published in 2023"

Fe-MOF derivative photocatalyst with advanced oxygen reduction capacity for indoor pollutants removal

Abstract: Fe-MOF derivative (M-300) is successfully prepared by thermal treatment using MIL-100(Fe) as a precursor. The resulting M-300 catalyst displays excellent performance in the degradation of volatile organic compounds (VOCs) and the bacteriostasis to Escherichia coli under the visible light. In addition, the catalyst also exhibits the capacity of acetaldehyde degradation under actual sunlight. The high activity is attributed to the abundant exposure of the unsaturated Fe2+ active site, which can significantly promote the transfer of photogenerated electron and the oxygen reduction process, thus improving the efficiency of the entire photocatalytic oxidation reaction. This work shows the application prospect of MOFs derivatives in the field of indoor photocatalytic purification, and also provides a new insight into the study of catalyst modification in photocatalytic degradation of pollutants.

Advances in Gelatin Bioinks to Optimize Bioprinted Cell Functions

Abstract: Gelatin is a widely utilized bioprinting biomaterial due to its cell-adhesive and enzymatically cleavable properties, which improve cell adhesion and growth. Gelatin is often covalently cross-linked to stabilize bioprinted structures, yet the covalently cross-linked matrix is unable to recapitulate the dynamic microenvironment of the natural extracellular matrix (ECM), thereby limiting the functions of bioprinted cells. To some extent, a double network bioink can provide a more ECM-mimetic, bioprinted niche for cell growth. More recently, gelatin matrices are being designed using reversible cross-linking methods that can emulate the dynamic mechanical properties of the ECM. This review analyzes the progress in developing gelatin bioink formulations for 3D cell culture, and critically analyzes the bioprinting and cross-linking techniques, with a focus on strategies to optimize the functions of bioprinted cells. This review discusses new cross-linking chemistries that recapitulate the viscoelastic, stress-relaxing microenvironment of the ECM, and enable advanced cell functions, yet are less explored in engineering the gelatin bioink. Finally, this work presents the perspective on the areas of future research and argues that the next generation of gelatin bioinks should be designed by considering cell-matrix interactions, and bioprinted constructs should be validated against currently established 3D cell culture standards to achieve improved therapeutic outcomes.

Fine scale mapping of laurentian mixed forest natural habitat communities using multispectral naip and uav datasets combined with machine learning methods

Abstract: Natural habitat communities are an important element of any forest ecosystem. Mapping and monitoring Laurentian Mixed Forest natural communities using high spatial resolution imagery is vital for management and conservation purposes. This study developed integrated spatial, spectral and Machine Learning (ML) approaches for mapping complex vegetation communities. The study utilized ultra-high and high spatial resolution National Agriculture Imagery Program (NAIP) and Unmanned Aerial Vehicle (UAV) datasets, and Digital Elevation Model (DEM). Complex natural vegetation community habitats in the Laurentian Mixed Forest of the Upper Midwest. A detailed workflow is presented to effectively process UAV imageries in a dense forest environment where the acquisition of ground control points (GCPs) is extremely difficult. Statistical feature selection methods such as Joint Mutual Information Maximization (JMIM) which is not that widely used in the natural resource field and variable importance (varImp) were used to discriminate spectrally similar habitat communities. A comprehensive approach to training set delineation was implemented including the use of Principal Components Analysis (PCA), Independent Components Analysis (ICA), soils data, and expert image interpretation. The developed approach resulted in robust training sets to delineate and accurately map natural community habitats. Three ML algorithms were implemented Random Forest (RF), Support Vector Machine (SVM), and Averaged Neural Network (avNNet). RF outperformed SVM and avNNet. Overall RF accuracies across the three study sites ranged from 79.45-87.74% for NAIP and 87.31-93.74% for the UAV datasets. Different ancillary datasets including spectral enhancement and image transformation techniques (PCA and ICA), GLCM-Texture, spectral indices, and topography features (elevation, slope, and aspect) were evaluated using the JMIM and varImp feature selection methods, overall accuracy assessment, and kappa calculations. The robustness of the workflow was evaluated with three study sites which are geomorphologically unique and contain different natural habitat communities. This integrated approach is recommended for accurate natural habitat community classification in ecologically complex landscapes.

Doppler Shift Effect as a Possible Explanation to the Hubble-Lemaitre Constant Tension

Abstract: The difference in the Hubble-Lemaitre constant determined by different probes is a yet unexplained observation. This paper proposes the contention that the tension could be explained by the Doppler shift effect of Ia supernovae or Cepheids, driven by the rotational velocity of their host galaxies relative to the rotational velocity of the Milky Way. While the effect of the Doppler shift is expected to be mild, observations show that it can lead to systematic differences in the apparent brightness, and consequently the estimated distances. A simple experiment is done by repeating a previous analysis. When using the original set of supernovae, $H_o$ is 73.758$\pm$1.943 km/s/Mpc. When using a subset of supernovae such that the host galaxies rotate in the same direction as the Milky Way, $H_o$ drops sharply to 69.049$\pm$3.42 km/s/Mpc, showing a far milder tension with the $H_o$ determined by the CMB. When using a subset of Ia supernovae that rotate in the opposite direction, Ho does not decrease, but instead it increases the Ho tension to 74.182$\pm$3.2. Further analysis will be required to determine the link between Ho observed by using Ia supernovae or Cepheids and the rotational velocity of the host galaxies.

Single-mode quasi PT-symmetric laser with high power emission

Abstract: Large-area lasers are practical for generating high output powers. However, this often comes at the expense of lower beam quality due to the introduction of higher-order modes. Here, we experimentally demonstrate a new type of electrically pumped, large-area edge-emitting lasers that exhibit a high power emission (∼0.4 W) and a high-quality beam (M2∼1.25). These favorable operational characteristics are enabled by establishing a quasi PT-symmetry between the second-order mode of a large area two-mode laser cavity and that of a single-mode auxiliary partner cavity, i.e., by implementing a partial isospectrality between the two coupled cavities. This in turn enlarges the effective volume of the higher-order modes. As a result, a selective pump applied via current injection into the main laser cavity can provide a stronger modal gain to the fundamental mode, and thus lead to lasing in the single mode regime after filtering out higher order transverse modes. The reported experimental results confirm this intuitive picture and are in good agreement with both theoretical and numerical analysis. Above all, the employed material platform and fabrication process are compatible with the industrial standards of semiconductor lasers. This work provides the first clear demonstration, beyond previous proof-of-concept studies, of the utility of PT-symmetry in building laser geometries with enhanced performance and, at the same time, useful output power levels and emission characteristics.

Mass Transport in Conformally Coated Porous Materials

Abstract: Coating 3D printed plastic materials has been shown to be an effective method of reducing outgassing [1].

Mechanism of Imperfect Atomic Layer Deposition in Porous Materials

Abstract: Polymer-based materials used for 3D printing are often porous [1].

Dispersed Nanostructured Thin Film (dNSTF) Catalyst

Abstract: Typical proton exchange membrane fuel cell (PEMFC) electrodes are multi-micron thick, porous films comprised of platinum dispersed over nm-scale carbon particles bound by ion conducting polymer, (ionomer).

Multilevel computational investigation into the dynamics and reaction mechanisms of non-heme iron and 2-oxoglutarate dependent enzymes

Abstract: Computational chemistry methods have been extensively applied to investigate biological systems. This dissertation utilizes a multilevel computational approach to explore the dynamics and reaction mechanisms of two groups of enzymes belonging to non-heme Fe(II) and 2-oxoglutarate (2OG) dependent superfamily – histone lysine demethylases from class 7 and ethylene forming enzyme (EFE). Chapter 2 uncovers the role of conformational dynamics in the substrate selectivity of histone lysine demethylases 7A and 7B. The molecular dynamics (MD) simulations of the two enzymes revealed the importance of linker flexibility and dynamics in relative orientations of the reader (PHD) and the catalytic (JmjC) domains. Chapter 3 describes the use of combined quantum mechanics/molecular mechanics (QM/MM) and MD simulations to explore the reaction mechanism of histone lysine demethylases 7B (PHF8), including dioxygen activation, 2OG binding modes, and substrate demethylation steps. Importantly, the calculations imply the rearrangement of the 2OG C-1 carboxylate prior to dioxygen binding at a five-coordination stage in catalysis, highlighting the dynamic nature of the non-heme Fe-center. Chapter 4 develops a computational framework for identifying second coordination sphere (SCS) and especially long range (LR) residues relevant for catalysis through dynamic cross correlation analysis (DCCA) using the PHF8 as a model oxygenase and explores their effects on the rate determining hydrogen atom transfer step. The results from the QM/MM calculations suggest that DCCA can identify non-active site residues relevant to catalysis. Chapter 5 explores the unique catalytic mechanism of EFE. In particular, the study elucidates the atomic and electronic structure determinants that distinguish between ethylene formation and L-Arg hydroxylation reaction mechanisms in the EFE. The results indicated that synergy between the conformation of L-Arg and the coordination mode of 2OG directs the reaction toward ethylene formation or L-Arg hydroxylation. Chapter 6 demonstrates that applying an external electric field (EEF) along the Fe-O bond in the EFE·Fe(III)·OO.-·2OG·L-Arg complex can switch the EFE reactivity between L-Arg hydroxylation and ethylene generation. Overall, applying an EEF on EFE indicates that making the intrinsic electric field of EFE less negative and stabilizing the off-line binding of 2OG might increase ethylene generation while reducing L-Arg hydroxylation. Chapter 7 probes the role of the protein environment in modulating the dioxygen diffusion and binding and thus ultimately contributing to the diverging reactivities of PHF8 and EFE. Overall, the results of this dissertation together highlight the several catalytic strategies utilized by the non-heme Fe(II) and 2OG dependent enzymes for achieving their reaction outcomes. In the longer term, the results can be used to modulate the activities of these enzymes either through enzyme redesign or the generation of enzyme-selective inhibitors.

Neuroevolution and Machine Learning Research Applied to Connected Automated Vehicle and Powertrain Control

Abstract: This dissertation focuses on advancing Predictive Energy Management (PrEM) functions applied to modern connected and automated vehicles (CAV) cohorts. PrEM aims to utilize connectivity and ADAS functions to adaptively minimize vehicle energy consumption in a wide array of operations, extending the original control designed around a reduced set of test cycle procedures to adapt to real-world stochastic operating conditions. This research document is built upon three journal publications covering two PrEM schemes; the global cohort and local vehicle optimization paths. Both optimal control solutions are generated using various Neuroevolution centric processes.

DFT-aided machine learning-based discovery of magnetism in Fe-based bimetallic chalcogenides

Abstract: Abstract With the technological advancement in recent years and the widespread use of magnetism in every sector of the current technology, a search for a low-cost magnetic material has been more important than ever. The discovery of magnetism in alternate materials such as metal chalcogenides with abundant atomic constituents would be a milestone in such a scenario. However, considering the multitude of possible chalcogenide configurations, predictive computational modeling or experimental synthesis is an open challenge. Here, we recourse to a stacked generalization machine learning model to predict magnetic moment (µB) in hexagonal Fe-based bimetallic chalcogenides, Fe x A y B; A represents Ni, Co, Cr, or Mn, and B represents S, Se, or Te, and x and y represent the concentration of respective atoms. The stacked generalization model is trained on the dataset obtained using first-principles density functional theory. The model achieves MSE, MAE, and R 2 values of 1.655 (µB) 2 , 0.546 (µB), and 0.922 respectively on an independent test set, indicating that our model predicts the compositional dependent magnetism in bimetallic chalcogenides with a high degree of accuracy. A generalized algorithm is also developed to test the universality of our proposed model for any concentration of Ni, Co, Cr, or Mn up to 62.5% in bimetallic chalcogenides.

Toward deep learning emulators for modeling the large-scale structure of the universe

Abstract: Multi-billion dollar cosmological surveys are being conducted almost every decade in today’s era of precision cosmology. These surveys scan vast swaths of sky and generate tons of observational data. In order to extract meaningful information from this data and test these observations against theory, rigorous theoretical predictions are needed. In the absence of an analytic method, cosmological simulations become the most widely used tool to provide these predictions in order to test against the observations. They can be used to study covariance matrices, generate mock galaxy catalogs and provide ready-to-use snapshots for detailed redshift analyses. But cosmological simulations of matter formation in the universe are one of the most computationally intensive tasks. Faster but equally reliable tools that could approximate these simulations are thus desperately needed. Recently, deep learning has come up as an innovative and novel tool that can generate numerous cosmological simulations orders of magnitude faster than traditional simulations. Deep learning models of structure formation and evolution in the universe are unimaginably fast and retain most of the accuracy of conventional simulations, thus providing a fast, reliable, efficient, and accurate method to study the evolution of the universe and reducing the computational burden of current simulation methods.

Characterizing drought adaptations, phenotypic plasticity, and fixed gene expression patterns within quercus

Abstract: This dissertation was written on topics related to the genus Quercus with a primary focus on Quercus ellipsoidalis (northern pin oak) and Quercus rubra (northern red oak). Within this dissertation are chapters related to the setup of experimental common gardens within the Ford and Kellogg experimental forest, a literature review describing drought adaptations of Quercus sect. Lobatae (red oak group), identification of transcription factors within the Q. robur (English oak) and Q. rubra genomes, a study comparing leaf trait phenotypic plasticity of Q. ellipsoidalis and Q. rubra, and an RNA-seq experiment studying ecological speciation between Q. ellipsoidalis and Q. rubra. Within these studies, I found that Q. ellipsoidalis and Q. rubra have similar leaf trait phenotypic plasticity, and unique molecular phenotypes related to upregulation of genes related to photosynthesis and innate immune response, respectively. Within the Q. rubra genome, I identified multiple regions of transcription factor gene clusters that could have a significant role related to drought adaptation for this species.

Enriched microbial consortia from natural environments provide novel method for upcycling plastic waste

Abstract: Presently, polyethylene terephthalate (PET) plastic waste pollutes the environment, and global food production is inadequate to support the growing population. A system that upcycles plastic into edible microbial protein powder may solve both problems. Many microorganisms can utilize plastic to produce microbial biomass containing fats, nutrients, and proteins. Microbial plastic biodegradation is very slow; however, coupling biodegradation with chemical pre-processing may substantially increase degradation rates. Previous work has used bioengineered microbial isolates to upcycle plastic. In contrast, this work will use microbial communities to process pre-processed plastics. First, we enrich microorganisms from natural environments to obtain communities which grow on terephthalate and terephthalamide (products of PET pre-processing). We demonstrate that terephthalamide (thought to be antimicrobial) is biodegradable, and that microorganisms able to grow using terephthalamide and terephthalate are widespread. This finding shows that there is great potential for microbial degradation of pre-processed PET. Second, we demonstrate that the enriched microbial communities can degrade chemically pre-processed PET without added growth medium. Growth tests on additional depolymerized polymers (polyethylene, spandex, nylon, mylar, and polyurethane) showcase the versatility of communities. Thirdly, we explore the mechanism of versatility by investigating the roles of generalist and specialist species in the microbial communities. Results showed that the coexistence of specialists and generalists may be essential to the microbial community’s ability to flexibly biodegrade mixed plastic waste streams. This work shows that using microbial communities, instead of isolates, to process plastic waste results in increased flexibility to process multiple plastic wastes.

The energy transition through an energy justice approach: a pumped underground storage hydro study through social, economic, and technical lens

Abstract: Electricity is projected to become the predominant form of energy carrier by 2050, and all major energy services will directly or indirectly depend on electricity. Intermittent renewable electricity (RE) generation technologies will likely serve as the primary means for the transition. For this reason, tackling the intermittency of RE technologies and energy storage requirements for balancing generation from intermittent sources with energy demand is considered a key element in supporting this energy transition. Pumped Underground Storage Hydro (PUSH) is one energy storage technology that could provide the required support for the RE transition. This dissertation studies PUSH comprehensively by including social dimensions in current predominant techno-economic models of valuing energy technologies. The dissertation focuses on integrating the energy justice concept in the energy transition to explore the role of the novel application of PUSH when developed in abandoned mines as an electricity storage technology participating in the restructured/liberalized electricity markets. To achieve the stated objective, the dissertation answers the following overarching research question: What are the energy justice implications of deploying Pumped Underground Storage Hydro (PUSH) developed in abandoned underground mines for supporting the energy transition in liberalized or restructured electricity markets in the U.S. states? As a whole, the dissertation demonstrates that to achieve energy transition that does not exacerbate the same systemic social injustice present in fossil fuel-based energy system, justice dimensions should be incorporated into energy system design. This research provides an energy justice framework for policymakers to inform the participation of novel technologies such as PUSH in the electricity market. The dissertation further provides evidence that the lack of policies based on ethical considerations in the electricity market design exacerbates energy service crises. Electricity markets require an ethical framework for designing and valuing technologies participating in the liberalized/restructured electricity market.

Brain-Derived Small Extracellular Vesicles from Dahl Salt-Sensitive Rats with High Salt Diet Induce Inflammation and Oxidative Stress

Abstract: It has been reported that small extracellular vesicles (sEVs ≤ 200 nm) are implicated in the pathogenesis of multiple diseases including hypertension. However, the role of brain-derived sEVs in the development of salt sensitive hypertension (SSHTN) remains unclear. We hypothesize that brain-derived sEVs from high salt diet-treated rats can induce inflammation and oxidative stress in the central nervous system (CNS). To test this hypothesis, brain-derived sEVs of Dahl salt-sensitive rats with high salt (HS) diet (Dahl-HS-sEV) were used to treat primary brain neuronal cultures and microinjected into brain lateral ventricles, respectively, proinflammatory cytokines, chemokines, and oxidative stress markers were measured through real-time PCR or fluorescent probes. sEVs isolated from Sprague Dawley (SD) rats with normal salt (NS) diet (SD-NS-sEV) were used as a control. Briefly, we isolated sEVs from brain tissues using ultracentrifugation and identified sEVs with scanning electron microscopy, dynamic light scattering and western blots. Primary neurons derived from neonatal SD rats were incubated with either Dahl-HS-sEV (4μg/mL), or SD-NS-sEV (4μg/mL) for 24 h. The mRNA levels of inflammatory factors, neuronal activity indicator (c-Fos) and NADPH oxidase subunits (CYBA and CYBB) were tested by real-time PCR. Results showed that Dahl-HS-sEV incubation increased mRNA levels of inflammatory cytokines including TNFα (2.3-fold) and IL1β (3.7-fold) with statistical significance (P<0.05). It also significantly increased (P<0.05) mRNA levels of chemokines including CCL2 (2.4-fold), CCL5 (2.1-fold), and CCL12 (4.2-fold). In addition, Dahl-HS-sEV treatment increased mRNA levels of transcription regulator, NF-κB (1.4-fold), and neuronal activation marker, c-FOS (1.3-fold), as well as CYBA (1.7-fold), in primary neurons, compared to SD-NS-sEV-treated cells (P<0.05). We further tested mitochondrial reactive oxygen species (ROS) levels using fluorescent probes in primary neurons. Confocal images showed that Dahl-HS sEV significantly increased mitochondrial ROS levels, with total fluorescence intensity increased 1.6-fold relative to SD-NS-sEV treatment (P<0.01). Subsequently, we tested the effect of sEVs on the inflammatory cytokine marker expression in the brain. SD-NS rats received intracerebroventricular injection of either Dahl-HS-sEV (5.5 μg /rat, n=4) or SD-NS-sEV (5.5 μg/rat, n=4) and euthanized 6 h after injection. Their brains were removed and paraventricular nucleus (PVN) were punched out. RNAs were isolated from PVN tissues and used for real-time PCR assessment. Results showed that Dahl-HS-sEV significantly increased (P<0.05) PVN mRNA levels of IL1β (4.3-fold), CCL5 (2.6-fold), IL-6 (3.4-fold) and NOS2 (5.2-fold) in SD-NS rats 6 h after injection. These results suggested that in SSHTN, brain-derived sEVs may induce central inflammation and oxidative stress, which in turn results in an elevation of arterial blood pressure. This work was supported by NIH grant R01HL 163159 (Shan) and R15HL 150703 (Shan), and Portage Health Foundation Mid-Career Award (Shan). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Boron nitride nanostructures: synthesis, characterization, and application in photovoltaics and biomedicine

Abstract: The popularity of boron nitride nanomaterials for their application in areas like electronics, photovoltaic, biomedical, automobile, and so on has increased in the recent era. Some unique properties make BNNTs outstanding for biomedical and photovoltaic applications, such as their optical transparency, electrical insulation, and biological compatibility. Hence large-scale synthesis of high-purity small-diameter BNNTs is required. On the other hand, research on the origin of excitation-dependent fluorescence (EDF) in BN dots is in its infancy. So, in this dissertation, we studied the growth mechanism of high-purity BNNTs with diameters smaller than 30nm. Additionally, we synthesized two different types of BN dots [sonication-assisted solvothermal (ss) BN dots and liquid nitrogen (LN) exfoliated BN dots], and studied their fluorescent properties. For the first time, we exploited the EDF property of the ss BN dots for solar cells. Here, we define ss BN dots as a pseudo-core/shell structure of DMA-CO and BN nanosheets and use them as the solar absorber (active layers, AL) to yield an efficiency of 1.51%. Our work is the first demonstration using biological and environmentally friendly colloidal nanomaterials for solar cells. In addition, we determined, for the first time, the biocompatible doses of ss BN dots for use in live HeLa cells. We found ss BN dots are suitable for live cell staining at doses below <10mg/mL for four consecutive days. We further investigated the photophysical properties of ss BN dots as fluorescent probes. We also demonstrated bright fluorescent staining of live HeLa cells using high-brightness fluorophores (HBFs) produced by our collaborators based on BN dots.

Theoretical investigation on optical properties of 2d materials and mechanical properties of polymer composites at molecular level

Abstract: The field of two-dimensional (2D) layered materials provides a new platform for studying diverse physical phenomena that are scientifically interesting and relevant for technological applications. Theoretical predictions from atomically resolved computational simulations of 2D materials play a pivotal role in designing and advancing these developments. The focus of this thesis is 2D materials especially graphene and BN studied using density functional theory (DFT) and molecular dynamics (MD) simulations. In the first half of the thesis, the electronic structure and optical properties are discussed for graphene, antimonene, and borophene. It is found that the absorbance in (atomically flat) multilayer antimonene (group V) is comparable to or greater than that for multilayer borophene (group III) and graphene (group IV). The number of layers has a substantial impact on the electrical and optical properties of graphene, antimonene, and borophene. Unlike graphene and antimonene, however, multilayer δ6-borophene exhibits extremely anisotropic electrical and optical characteristics. Overall, our findings imply that multilayer graphene and antimonene are good optical absorbers, particularly in the infrared region of the spectrum, and could be employed as a coating to protect against mid-IR tunable lasers. However, borophene because of its high optical transparency and good metallicity, could be a promising choice for transparent conductive 2D materials with applications in photovoltaics, performance-controlled optoelectronic devices, and touch displays. Molecular-level simulations for monomers with graphene/BN were undertaken to relate the interfacial features with the corresponding mechanical response in terms of strain and stiffness. The results show that the nature of bonding at the interface determines the interaction strength between resin (or hardener) and graphene and that the mechanical response follows the hierarchical order of the interaction strength at the interface. In addition, the change in polarity from graphene to BN monolayer also leads to improved interfacial strength as well as increased transverse stiffness at the molecular level for both resins and hardeners. We have also studied the effect of BN reinforcement with representative cases of cyanate esters, epoxy, and bismaleimide (BMI) resins using molecular dynamics to characterize the bulk level properties of reinforcement/polymer interface. Calculations simulating pull-apart transverse tension experiments find that the non-fluorinated ester interface exhibits higher stiffness and toughness than the fluorinated interface. On the other hand, the epoxy/BN interface is predicted to have significantly lower toughness (or resistance to fracture) than the BMI/BN interface. BMI, thus, appears to be the polymer matrix of choice when considering the BN nanomaterials as reinforcement compared to either cyanate ester or epoxy polymers for structural applications. These results based on molecular simulations emphasize the need to use computational modeling to efficiently and accurately determine molecular-level polymer/surface combinations that yield optimal composite material mechanical performance. This is especially true when designing and developing high-performance composites with nanoscale reinforcement.

Multiscale molecular modeling studies of the dynamics and catalytic mechanisms of iron(ii)- and zinc(ii)-dependent metalloenzymes

Abstract: Enzymes are biological systems that aid in specific biochemical reactions. They lower the reaction barrier, thus speeding up the reaction rate. A detailed knowledge of enzymes will not be achievable without computational modeling as it offers insight into atomistic details and catalytic species, which are crucial to designing enzyme-specific inhibitors and impossible to gain experimentally. This dissertation employs advanced multiscale computational approaches to study the dynamics and reaction mechanisms of non-heme Fe(II) and 2-oxoglutarate (2OG) dependent oxygenases, including AlkB, AlkBH2, TET2, and KDM4E, involved in DNA and histone demethylation. It also focuses on Zn(II) dependent matrix metalloproteinase-1 (MMP-1), which helps collagen degradation. Chapter 2 investigates the substrate selectivity and dynamics on the enzyme-substrate complexes of DNA repair enzymes, AlkB and FTO. Chapter 3 unravels the mechanisms and effects of dynamics on the demethylation of 3-methylcytosine substrate by AlkB and AlkBH2 enzymes. The results imply that the nature of DNA and conformational dynamics influence the electronic structure of the iron center during demethylation. Chapter 4 delineates how second-coordination and long-range residue mutations affect the oxidation of 5-methylcytosine substrate to 5-hydroxymethylcytosine by TET2 enzyme. The results reveal that mutations affect DNA binding/interactions and the energetic contributions of residues stabilizing key catalytic species. Chapter 5 describes the reparation of unnatural alkylated substrates by TET2, their effects on second-coordination interactions and long-range correlated motions in TET2. The study reveals that post-hydroxylation reactions occur in aqueous solution outside the enzyme environment. Chapter 6 establishes how applying external electric fields (EEFs) enhances specificity of KDM4E for C—H over N—H activation during dimethylated arginine substrate demethylation. The results reveal that applying positive EEFs parallel to Fe=O bond enhances C—H activation rate, while inhibiting the N—H one. Chapter 7 addresses the formation of catalytically competent MMP-1·THP complex of MMP-1. The studies reveal the role of MMP-1’s catalytic domain a-helices, the linker, and changes in coordination states of catalytic Zn(II) during the transition. Overall, the presented results contribute to the in-depth understanding of the fundamental mechanisms of the studied enzymes and provide a background for developing enzyme-specific inhibitors against the associated disorders and diseases.

The 2019 Raikoke eruption as a testbed for rapid assessment of volcanic atmospheric impacts by the Volcano Response group

Abstract: Abstract. The 21st June 2019 Raikoke eruption (48° N,153° E) generated one of the largest amounts of sulfur emission to the stratosphere since the 1991 Mt Pinatubo eruption. Satellite measurements indicate a consensus best estimate of 1.5 Tg for the sulfur dioxide (SO2) injected at an altitude of around 14–15 km. The peak northern hemisphere mean 525 nm Stratospheric Aerosol Optical Depth (SAOD) increased to 0.025, a factor of three higher than background levels. The Volcano Response (VolRes) initiative provided a platform for the community to share information about this eruption, which significantly enhanced coordination efforts in the days after the eruption. A multi-platform satellite observation sub-group formed to prepare an initial report to present eruption parameters including SO2 emissions and their vertical distribution for the modelling community. It allowed to make the first estimate of what would be the peak in SAOD one week after the eruption using a simple volcanic aerosol model. In this retrospective analysis, we show that revised volcanic SO2 injection profiles yield a higher peak injection of the SO2 mass. This highlights difficulties in accurately representing the vertical distribution for moderate SO2 explosive eruptions in the lowermost stratosphere due to limited vertical sensitivity of current satellite sensors (+/- 2 km accuracy) and low horizontal resolution of lidar observations. We also show that the SO2 lifetime initially assumed in the simple aerosol model was overestimated by 66 %, pointing to challenges for simple models to capture how the life cycle of volcanic gases and aerosols depends on the SO2 injection magnitude, latitude and height. Using revised injection profile, modelling results indicate a peak northern hemisphere monthly mean SAOD at 525 nm of 0.024, in excellent agreement with observations, associated with a global monthly mean radiative forcing of -0.17 W/m2 resulting in an annual global mean surface temperature anomalies of -0.028 K. Given the relatively small magnitude of the forcing, it is unlikely that the surface response can be dissociated from surface temperature variability.