UMBC Chemical, Biochemical & Environmental Engineering Department

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Faculty in our department focus their research in nine core areas encompassing biological, environmental, and educational research. Our department offers an undergraduate course of study leading to a B.S. in Chemical Engineering through three tracks of study: (i) Traditional Track, (ii) Biotechnology and Bioengineering Track and (iii) Environmental Engineering and Sustainability track. We also offer both M.S. and Ph.D. degrees through two different graduate programs. Details can be found on our website: http://www.umbc.edu/cbe

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Recent Submissions

Now showing 1 - 20 of 287
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    Recent Developments in Bioprocess Monitoring Systems
    (Springer, 2024-01-11) Rahmatnejad, Vida; Wei, Yunqian; Rao, Govind
    Bioprocess monitoring systems are essential tools for achieving an optimal manufacturing process for pharmaceuticals. In recent years, there have been advancements in the development of these systems. One of the most notable developments in bioprocess monitoring systems is the use of biosensors, which can detect and measure biological molecules such as enzymes and proteins. The use of imaging technologies, such as microscopy and flow cytometry, has allowed for the non-invasive monitoring of cell culture, providing valuable information for process optimization. Other non-invasive monitoring systems are being developed with the aim of reducing the risk of contamination. Another important advancement is the use of machine learning and artificial intelligence in bioprocess monitoring systems. These technologies have the ability to learn from historical data and predict future outcomes, which can be beneficial for optimizing bioprocesses in real-time. Overall, recent advancements in sensor technology can help in improving the efficiency and effectiveness of bioprocessing. The integration of advanced sensing technologies, and machine learning can provide valuable insights and improve the ability to monitor and control bioprocesses online. In this chapter, we will discuss recent advancements in bioprocess monitoring systems, specifically for monoclonal antibody and cell and gene therapy manufacturing processes.
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    Monte Carlo simulations of water pollutant adsorption at parts-per-billion concentration: A study on 1,4-dioxane
    (2024-01-16) Sharlin, Samiha; Lozano, Rodrigo; Josephson, Tyler R.
    1,4-dioxane is an emerging water pollutant with high production volumes and a probable human carcinogen. The incompetence of conventional treatment processes demonstrates a need for an effective remediation strategy. Crystalline nanoporous materials are cost-effective adsorbents due to their high capacity and selective separation in mixtures. This study explores the potency of all-silica zeolites. These zeolites are highly hydrophobic and can preferentially adsorb nonpolar molecules from mixtures. We investigated six zeolite frameworks (BEA, EUO, FER, IFR, MFI, MOR) using Monte Carlo simulations in the Gibbs ensemble. The simulations indicate high selectivity by FER and EUO, especially at low pressures, which we attribute to pore sizes and shapes with more affinity to 1,4-dioxane. We also demonstrate a Monte Carlo simulation workflow using gauge cells to model the adsorption of an aqueous solution of 1,4-dioxane at 0.35 ppb concentration. We quantify 1,4-dioxane and water coadsorption and observe selectivities ranging from 1.1 x 10^5 in MOR to 8.7 x 10^6 in FER. We also demonstrate that 1,4-dioxane is in the infinite dilution regime in both the aqueous and adsorbed phases at this concentration. This simulation technique can be extended to model other emerging water contaminants such as per- and polyfluoroalkyl substances (PFAS), chlorates, and others, which are also found in extremely low concentrations.
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    Global nuclear radiation monitoring using plants
    (SPIE, 2015-05-13) Islam, Mohammad; Romero-Talamás, Carlos; Kostov, Dan; Wang, Wanpeng; Liu, Zhongchi; Hussey, Daniel S.; Baltic, Eli; Jacobson, David L.; Gu, Jerry; Choa, Fow-Sen
    Plants exhibit complex responses to changes in environmental conditions such as radiant heat flux, water quality, airborne pollutants, soil contents. We seek to utilize the natural chemical and electrophysiological response of plants to develop novel plant-based sensor networks. Our present work focuses on plant responses to high-energy radiation – with the goal of monitoring natural plant responses for use as benchmarks for detection and dosimetry. For our study, we selected a plants cactus, Arabidopsis, Dwarf mango (pine), Euymus and Azela. We demonstrated that the ratio of Chlorophyll a to Chlorophyll b of the leaves has changed due to the exposure gradually come back to the normal stage after the radiation die. We used blue laser-induced blue fluorescence-emission spectra to characterize the pigment status of the trees. Upon blue laser excitation (400 nm) leaves show a fluorescence emission in the red spectral region between 650 and 800nm (chlorophyll fluorescence with maxima near 690nm and 735 nm). Sample tree subjects were placed at a distance of 1m from NIST-certified 241AmBe neutron source (30 mCi), capable of producing a neutron field of about 13 mrem/h. This corresponds to an actual absorbed dose of ~ 1 mrad/h. Our results shows that all plants are sensitive to nuclear radiation and some take longer time to recover and take less. We can use their characteristics to do differential detection and extract nuclear activity information out of measurement results avoid false alarms produced environmental changes. Certainly the ultimate verification can be obtained from genetic information, which only need to be done when we have seen noticeable changes on plant optical spectra, mechanical strength and electrical characteristics.
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    Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images
    (PLOS, 2023-12-14) Moitra , Moumita; Alafeef , Maha; Narasimhan , Arjun; Kakaria , Vikram; Moitra, Parikshit; Pan, Dipanjan
    COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (CT) imaging has remained the first line of diagnosis for individuals infected with SARS-CoV-2. However, differentiating COVID-19 from other types of pneumonia and predicting associated cardiovascular complications from the same chest-CT images have remained challenging. In this study, we have first used transfer learning method to distinguish COVID-19 from other pneumonia and healthy cases with 99.2% accuracy. Next, we have developed another CNN-based deep learning approach to automatically predict the risk of cardiovascular disease (CVD) in COVID-19 patients compared to the normal subjects with 97.97% accuracy. Our model was further validated against cardiac CT-based markers including cardiac thoracic ratio (CTR), pulmonary artery to aorta ratio (PA/A), and presence of calcified plaque. Thus, we successfully demonstrate that CT-based deep learning algorithms can be employed as a dual screening diagnostic tool to diagnose COVID-19 and differentiate it from other pneumonia, and also predicts CVD risk associated with COVID-19 infection.
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    The Linkage Between Electro-Chemical Mechanical Instabilities in Battery Materials
    (Springer, 2023-11-20) Wable, Minal; Marckx, Bret; Çapraz, Ömer Özgür
    Battery chemistry must be diversified to achieve a sustainable energy landscape by effectively utilizing renewable energy sources. Alkali metal-ion, all-solid-state, metal–air batteries, and multivalent batteries offer unique cost, safety, raw material abundance, energy, and power density solutions. However, realizing these “beyond Li-ion batteries” must uncover their working principles and performance & property relationships. In this aspect, mitigating chemo-mechanical instabilities in the structure and surface of the electrodes plays a crucial role in their performance. Unfortunately, the coupling between electrochemical and mechanical interactions is often poorly understood due to a lack of operando characterization. This review article explains the working principles of curvature measurement and digital image correlation for measuring stress and strain generations in battery materials. We provided specific examples of how these operando mechanical measurements shed light on instabilities in alkali metal ion electrodes, solid electrolytes, Li-O₂ batteries, and aqueous Zn-ion batteries. Operando mechanical measurements offer an effective way to map changes in the physical fingerprint of the battery materials, therefore providing crucial information to elucidate instabilities in battery materials.
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    Probing Electrochemical Strain Generation in Sodium Chromium Oxide (NaCrO2) Cathode in Na-ion Batteries during Charge/Discharge
    (2023-11-15) Wable, Minal; Bal, Batuhan; Çapraz, Ömer Özgür
    Sodium chromium oxide, NaCrO₂, exhibits promising features as a cathode electrode in Na-ion batteries, yet it encounters challenges with its capacity fading and poor cycle life. NaCrO₂ undergoes multiple phase transitions during Na ion intercalation, eventually leading to chemical instabilities and mechanical deformations. Here, we employed the digital image correlation (DIC) technique to probe electrochemical strain generation in the cathode during cycling via cyclic voltammetry and galvanostatic cycling. The electrode undergoes significant irreversible mechanical deformations in the initial cycle, and irreversibility decreases in the subsequent cycles. During desodiation and sodiation, the electrode initially undergoes volume contraction at lower state-of-(dis)charge followed by expansions at a higher state-of-(dis)charge. The similar progression between strain and capacitive derivatives points out the phase-transition-induced deformations in the electrode. The evolution of cumulative irreversible strains with cycling time indicates the irreversibility rising from the formation of cathode-electrolyte interphase layers. The study demonstrates valuable insights into mechanical deformations in NaCrO₂ electrodes during battery cycling, which is critical to engineer mechanically robust cathodes for Na-ion batteries.
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    Sustainable ammonium recovery from agricultural waste by Donnan dialysis
    (2023-01-01) Fleming, Michael Anthony; Blaney, Lee; Chemical, Biochemical & Environmental Engineering; Engineering, Civil and Environmental
    This dissertation focused on ammonium (NH4+) recovery from agricultural waste by Donnan dialysis. The primary goals were to improve water quality and provide a sustainable source of ammonium-based fertilizers. Donnan dialysis is a separation process that exploits electrochemical potential gradients between a concentrated draw solution and a waste solution separated by a semipermeable, ion-exchange membrane. My research advanced the state of the art by employing phosphate-based draw solutions that allow for in situ precipitation of valuable fertilizers within the draw chamber of the Donnan dialysis reactor. In particular, the process was designed to recover struvite (MgNH4PO4á6H2O), a slow-release fertilizer that produces less nutrient runoff and provides better crop yields than conventional fertilizers. The broader impacts of my work will help to minimize our dependence on energy-intensive production of ammonia fertilizers and reduce eutrophication from agricultural runoff. This dissertation had three specific aims. The first aim was to experimentally measure fundamental Donnan dialysis parameters, namely the separation factors and diffusion coefficients for ammonium in three different membranes: CMI-7000; Nafion 117; and Selemion CMVN. The CMI-7000 membrane had a higher ion-exchange capacity than Nafion 117, favoring NH4+ uptake; however, Nafion 117 was thinner than CMI-7000, suggesting faster diffusion. Selemion CMVN was a thinner membrane than Nafion 117 with an ion-exchange capacity similar to CMI-7000, which suggested better potential for NH4+ recovery. For the second aim, I incorporated the Selemion CMVN membrane into Donnan dialysis reactors to recover NH4+ from synthetic wastewater as struvite. Chelating agents were used to decrease recovery of calcium phosphate products, which are not useful fertilizers. The process was further optimized for NH4+ recovery from real agricultural waste, namely poultry litter, by employing clinoptilolite in the draw chamber of the Donnan reactors. The third aim was to improve struvite collection efficiency with natural coagulants following phosphorus recovery from poultry litter. The efficacy of chitosan, bentonite, and alginate to improve coagulation and flocculation of struvite particles from poultry litter extracts was investigated and alginate was determined to be most effective.
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    ALTERNATIVE APPROACHES TO INSULIN MANUFACTURING USING CELL-FREE SYSTEMS
    (2023-01-01) Borhani, Shayan; Rao, Govind; Chemical, Biochemical & Environmental Engineering; Engineering, Chemical and Biochemical
    The modular nature of cell-free protein synthesis (CFPS) has resulted in a paradigm shift in the way scientists can design, discover, and manufacture therapeutic proteins. This dissertation reports on three advances made in point-of-care (POC) manufacturing of therapeutics by using CFPS systems to address current challenges around pandemic preparedness and future therapeutic shortages. First, the utility of both prokaryotic and eukaryotic cell-free systems (CFS) was exploited to synthesize human proinsulin to assess the extent of post-translational modification and product quality achieved by each CFS. Second, an Òon-columnÓ purification approach was developed for post-translational conversion of proinsulin into mature human insulin using a specialized set of proteases. Third, a technoeconomic model was utilized to assess the cost effectiveness of cell-free manufacturing of insulin in comparison to the current state of the art. A less complex protein target, griffithsin (GRFT), was also tested using similar approaches. According to a 2022 study, approximately 1.3 million diabetic Americans are currently rationing insulin due to cost. Additionally, microsimulations have shown that by the year 2030, half of Type II diabetic patients are expected to face challenges accessing insulin. Currently, insulin manufacturing takes place in good manufacturing practice (GMP) facilities which utilize in vivo fermentations expressing proinsulin using E. coli and P. pastoris cell lines. While efficient, these fermentations take days to weeks to complete and are not achievable at the POC. For this reason, the in vitro approach enabled by CFPS systems offers a significant advantage, as it is more rapid and can be utilized for production of therapeutics at the POC and on-demand. This dissertation reports the reproducible and soluble expression of difficult-to-express proinsulin, as well as antiviral GRFT, in under 24h using both E. coli and ALiCE? (N. tabacum) CFS. Specifically, a series of cell-free reaction parameters were adjusted including, plasmid concentration, temperature, reaction time, chaperone concentration, and redox potential to achieve optimal protein titer. This dissertation also highlights a cost reduction in purification and post-translational conversion of proinsulin into recombinant human insulin by using an Òon-columnÓ immobilized metal affinity chromatography (IMAC) approach. The results suggest that by reducing the number of unit operations using this approach, a lower cost of goods sold (COGS) for insulin could be achievable with further optimization. To evaluate and optimize the manufacturing cost of the bioprocess outlined in this study, SuperPro Designer software was utilized to identify which components within the process were cost drivers. Additionally, a sensitivity analysis was performed around unit operations and parameters which were a source of increased cost and compared to the COGS of insulin using current manufacturing approaches. Keywords: human insulin; proinsulin; cell-free protein synthesis, CFPS; cell-free; transcription, translation, in vitro; protein engineering; biomanufacturing; bioprocess; point-of-care, POC; post-translational modification, PTM; biologics; pandemic preparedness; antivirals; next-generation biomanufacturing; proteases; enzymes; purification; immobilized metal affinity chromatography, IMAC
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    Identifying wastewater inputs to urban streams by monitoring fluorescent dissolved organic matter and contaminants of emerging concern
    (2023-01-01) Batista Andrade, Jahir Antonio; Blaney, Lee; Chemical, Biochemical & Environmental Engineering; Engineering, Civil and Environmental
    Failing sewer infrastructure introduces raw wastewater into streams, threatening public and ecological health. This dissertation presents new environmental forensics strategies to identify, locate, and quantify wastewater inputs to urban watersheds. First, analysis of fluorescent dissolved organic matter (FDOM) via excitation-emission matrix (EEM) spectroscopy and parallel factor analysis (PARAFAC) were proposed as tools to track hotspots of raw wastewater in low-order urban streams impacted by sanitary sewer leaks and overflows. Novel EEM-PARAFAC parameters, including the ratios of (i) microbial soluble product-like and humic acid-like fluorescence (R4/R5) and (ii) tryptophan-like and fulvic acid-like fluorescence (C4/C3), were developed and employed to monitor wastewater-like FDOM. The proposed EEM-PARAFAC metrics were externally validated by assessment of contaminants of emerging concern (CECs), including sucralose, antibiotics, and UV filters, and bacterial indicator organisms at select sampling sites. Second, a comprehensive study on the spatiotemporal distribution of EEM-PARAFAC components and CEC levels was conducted to assess compositional differences between sites in the main stem and tributaries of two urban watersheds. Principal component analysis (PCA) and cross-covariance analysis were applied to the EEM-PARAFAC and land cover datasets to determine relationships between FDOM sources, fate, and transport and impervious surfaces, sewer density, and septic system density. The outcomes of the PCA and cross-covariance studies suggested that geospatial data related to impervious surfaces and sewer density could be used to inform smart sampling strategies in areas most susceptible to failing sewer infrastructure. Finally, a multilinear model was developed and validated to predict wastewater content in urban streams using data extracted from fluorescence EEMs. The model was used to estimate the wastewater contents of 165 samples collected from an urban watershed, and the estimated wastewater contents ranged from 1% to 35%. The highest wastewater content was associated with an urban site known to be impacted by sanitary sewer leaks and flagged by earlier EEM and CEC analysis. The overall outcomes of this research provide alternative, rapid, and cost-effective methods to assess wastewater content in urban watersheds that do not receive wastewater effluent but are continuously affected by sanitary sewer leaks and overflows. These chemical, geospatial, and mathematical tools can also be used to estimate the potential impacts of failing sewer infrastructure on water quality in other locations.
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    NONINVASIVE PROCESS MONITORING FOR CELL THERAPY APPLICATIONS
    (2023-01-01) Rahmatnejad, Vida; Rao, Govind; Chemical, Biochemical & Environmental Engineering; Engineering, Chemical and Biochemical
    Cell therapies are therapies where cellular materials are injected, grafted, or implanted into the patient body to produce medicinal effects. As a growing field, cell therapy has demonstrated significant potential in the treatment of diseases ranging from diabetes and soft tissue wounds to cancer, nervous system, and genetic disorders. Despite the promising results from cell therapies, the manufacturing process of these therapies is associated with issues such as a lack of appropriate small-scale models and poorly defined manufacturing processes which contribute to the high cost of these therapies. Cell culture is the longest step in the manufacturing process of cell therapies, and the characteristics of cells could be affected by critical parameters in the cell culture environment, such as pH, dissolved carbon dioxide (DCO2), and dissolved oxygen (DO). Therefore, cell culture is one of the mostcritical steps in the manufacturing process of cell therapies because it defines the quality and efficacy of cell therapies. The integration of sensors with the manufacturing process helps in optimizing critical environmental parameters and mitigating problems at the early stages of the process. Therefore, bioreactors, as valuable platforms for cell culture processes, must be equipped with sensors to measure the critical process parameters and develop appropriate control methods. Despite the advantages that monitoring systems provide, their presence in the cell culture environment increases the risk of contamination. Avoiding contamination in the manufacturing process of cell therapies is of high importance because the final products in these processes are cells. Unlike other biologics, cells cannot be sterilized due to being fragile. Therefore, developing a noninvasive monitoring technique is beneficial because it helps eliminate the chance of contamination during the monitoring process. This dissertation is an effort to develop a technology capable of simultaneous monitoring of pH, DCO2, and DO without requiring direct contact with the cell culture environment. Sensors for monitoring pH, DCO2, and DO analytes were previously developed at CAST. The techniques were further developed to achieve noninvasive methods for monitoring pH, DCO2, and DO. The principles utilized in noninvasive techniques, the experimental studies, and the results are discussed in this report. Subsequently, the flow cell, the technology designed for simultaneous monitoring of pH, DCO2, and DO outside the bioreactor, is introduced. The flow cell was developed by combining the principles utilized in individual noninvasive techniques for monitoring pH, DCO2, and DO. The proposed flow cell prototype was investigated in multiple experiments, and the results from studies indicate the efficacy of flow cell in tracking changes inside the bioreactor.
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    EFFECTS OF TRANSITION METALS IN PARTICULATE MATTER ON OXIDATIVE POTENTIAL MEASUREMENTS USING PHOSPHATE-BASED ACELLULAR ASSAYS
    (2023-01-01) Yalamanchili, Jayashree; Hennigan, Christopher J Reed, Brian E; Chemical, Biochemical & Environmental Engineering; Engineering, Chemical and Biochemical
    Particulate matter (PM) is deleterious to human health. Transition metals are hypothesized to be toxic based on their oxidative potential (OP) to generate reactive oxygen species (ROS). Acellular assays use phosphate buffer to simulate human body conditions (pH 7.4, T 37 ¡C). However, the aqueous metals may undergo physical and chemical transformations in the assay matrix. The metal transformations during the assay could possibly be the reason for differences between chemical assays and epidemiological and toxicological studies of metal toxicity. Although metal complexation and precipitation with phosphate has been extensively studied in other systems interactions of metals with the phosphate buffer in acellular OP chemical assays have not been characterized. In this work, we first predicted the fate of transition metals in the dithiothreitol (DTT) assay matrix (phosphate buffer) using an equilibrium model and macroscopic experiments where aqueous metals were present at higher concentrations (> 50 ?M). We identified metal-phosphate buffer interactions, such as metal precipitation and oxidation. Thermodynamically, Fe(III), Fe(II), and Mn(II) mostly precipitate since the aqueous metal concentrations in PM and metal salt experiments exceed their solubilities, and Fe(II) oxidized to Fe(III) during the assay. At low metal concentration, the precipitation of aqueous metals was characterized for individual metals and a NIST reference standard (Urban PM, SRM-1648a). Multiple analyses, including microscopic analyses such as Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) confirmed the occurrence of precipitation and possible coprecipitation and/ or adsorption in the complex multi-component PM compared to individual aqueous metal salts. The effect of metal precipitation in ambient Baltimore PM and Urban PM (SRM-1648a) during the DTT assay were estimated for varying buffer concentrations (0.01 - 0.50 M). Sample concentration, composition, precipitate size, ionic strength, and phosphate concentration affected the OP measured by the phosphate based DTT assay. Our work provides clear evidence that OP assays need to be standardized in their procedure and offers guidance for how the identified artifact may be minimized. In light of the fact that other chemical and biological methods also use phosphate buffer to simulate human body conditions, this phenomenon may also extend to other assays such as Ascorbic Acid (AA) assay, and Glutathione (GSH) assay. The interactions of metal cations with the buffer matrix can be extended to other surrogate biological fluids that contain phosphates, such as simulated lung fluid, simulated body fluid, simulated saliva, simulated sweat, and simulated synovial fluid. The findings from this work will have broader implications for quantifying the toxicity and health effects of metals in PM.
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    Highly-Specific Single-Stranded Oligonucleotides and Functional Nanoprobes for Clinical Determination of Chlamydia Trachomatis and Neisseria Gonorrhoeae Infections
    (Wiley, 2023-10-23) Dighe, Ketan; Moitra, Parikshit; Gunaseelan, Nivetha; Alafeef, Maha; Jensen, Tor; Rafferty, Carla; Pan, Dipanjan
    Early detection of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) is the key to controlling the spread of these bacterial infections. An important step in developing biosensors involves identifying reliable sensing probes against specific genetic targets for CT and NG. Here, the authors have designed single-stranded oligonucleotides (ssDNAs) targeting mutually conserved genetic regions of cryptic plasmid and chromosomal DNA of both CT and NG. The 5′- and 3′- ends of these ssDNAs are differentially functionalized with thiol groups and coupled with gold nanoparticles (AuNP) to develop absorbance-based assay. The AuNPs agglomerate selectively in the presence of its target DNA sequence and demonstrate a change in their surface plasmon resonance. The optimized assay is then used to detect both CT and NG DNA extracted from 60 anonymized clinical samples with a clinical sensitivity of ∼100%. The limit of detection of the assays are found to be 7 and 5 copies/µL for CT and NG respectively. Furthermore, it can successfully detect the DNA levels of these two bacteria without the need for DNA extraction and via a lateral flow-based platform. These assays thus hold the potential to be employed in clinics for rapid and efficient monitoring of sexually transmitted infections.
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    Development and testing of novel functionalized polymeric thin-films for equilibrium passive sampling of PFAS compounds in water
    (Elsevier, 2023-10-16) Yan, Songjing; Murtadha, Batool; Foster, Gregory; Ghosh, Upal
    The demand for an accurate assessment of exposure and risk from the presence of per- and polyfluoroalkyl substances (PFAS) in the aquatic environment has created the need for new methods of measurements in water at sub-parts per trillion concentrations. We explored the concept of equilibrium passive sampling for PFAS using the strategy developed for other organic compounds. Equilibrium passive sampling should predict the concentration of the chemically labile fraction of PFAS in water based on equilibrium partitioning into the sampler, without the need for site-specific calibration. Our goals were to identify sampler materials with the potential to mimic PFAS partitioning into animals and sediments and provide reversible sorption in a time frame appropriate for in situ samplers. To achieve this goal, we tested a range of candidate materials, including three broad classes of polymers embedded with suitable sorbents for PFAS. The most promising synthesized thin films were activated carbon (AC) embedded in agarose, silica-bonded human serum albumin (s-HSA) embedded in Agarose, WAX embedded in cellulose acetate, and HLB embedded in PDMS, which yielded log sampler–water partition coefficients close to 3 for many PFAS compounds. Sampler equilibration time in sediments was approximately one week. Investigation of the isotherms suggested that the sorption can be described across the concentration range of interest, from the ng/L range commonly found in natural uncontaminated waters to the ug/L concentrations found in contaminated sites. Also, the selected films exhibited relatively rapid desorption of PFAS, indicating that the sorbents are capable of reversible, equilibrium measurements. The present study demonstrates a potential new approach to passive sampling of PFAS.
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    The brain-bone marrow axis: Implications for chronic traumatic brain injury and age-related neurodegeneration
    (2023-09-21) Wu, Junfang; Ritzel, Rodney; Li, Yun; Jiao, Yun; Doran, Sarah; Khan, Niaz; Henry, Rebecca; Brunner, Kavitha; Loane, David; Faden, Alan; Szeto, Gregory
    Introduction: It is well established that traumatic brain injury (TBI) causes acute and chronic alterations in systemic immune function and that systemic immune changes contribute to posttraumatic neuroinflammation and neurodegeneration. However, how TBI affects bone marrow (BM) hematopoietic stem cells/progenitors chronically and to what extent such changes may negatively impact innate immunity and neurological function has not been examined. Methods: To further understand the role of BM cell derivatives on TBI outcome, we generated BM chimeric mice by transplanting BM from chronically injured or sham (i.e., 90 days post-surgery) congenic donor mice into otherwise healthy, age-matched, irradiated hosts. Immune changes were evaluated by flow cytometry, multiplex ELISA, and NanoString technology. Moderate-to-severe TBI was induced by controlled cortical impact injury and neurological function was measured using a battery of behavioral tests. Results: TBI induced chronic alterations in the transcriptome of BM lineage-c-Kit+Sca1+ (LSK+) cells in wild type (WT) mice, including modified epigenetic and senescence pathways. After 8 weeks of reconstitution, no changes in BM or blood leukocyte number or composition were observed in TBI→WT chimeric mice compared to either SH→WT or non-irradiated WT control mice, suggesting normal engraftment. However, peripheral myeloid cells from TBI→WT mice showed significantly higher oxidative stress levels and reduced phagocytic activity, consistent with previous findings in WT TBI mice. TBI→WT mice also showed higher plasma concentrations of chemokines and displayed significant deficits in neurological function. At eight months after reconstitution, TBI→WT chimeric mice were leukopenic, with continued altered phagocytosis and oxidative stress responses, as well as persistent neurological deficits. NanoString gene expression analysis revealed BM-driven changes in neuroinflammation and neuropathology after 8 weeks and 8 months of reconstitution, respectively. Chimeric mice subjected to TBI at 8 weeks and 8 months post-reconstitution showed that longer reconstitution periods (i.e., time post-injury) were associated with increased microgliosis and leukocyte infiltration. Pre-treatment with a senolytic agent, ABT-263, significantly improved behavioral performance of aged mice at baseline, although it did not attenuate neuroinflammation in the acutely injured brain. Conclusion: TBI causes chronic activation and progressive dysfunction of the BM stem/progenitor cell pool, which drives long-term deficits in hematopoiesis, innate immunity, neurological function, as well as altered sensitivity to subsequent brain injury
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    Evaluation of green infrastructure using hydrologic modeling and high performance computing
    (2015) Andino-Nolasco, Elvis; Welty, Claire
    To develop a coupled groundwater-surface water model to evaluate the impacts of green infrastructure on groundwater resources in Philadelphia.
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    Fate of transition metals in PO₄-based in vitro assays: equilibrium modeling and macroscopic studies
    (Royal Society of Chemistry, 2021-01-01) Reed, Brian; Yalamanchili, Jayashree; Leach, Jennie; Hennigan, Christopher
    Transition metals are thought to be among the most toxic components in atmospheric particulate matter (PM) due to their role in catalyzing reactive oxygen species (ROS) formation. We show that precipitation of the transition metals Fe(II), Fe(III), and Mn(II) are thermodynamically favored in phosphate-based assays used to measure the oxidative potential (OP) – a surrogate for toxicity – of PM. Fe and Mn precipitation is likely to occur at extremely low metal concentrations (<0.5 μM), levels that are imperceptible to the naked eye. The concentration of each metal (other than Cu) in aqueous PM filter extracts often exceeds the solubility limit in OP assays, indicating favorable thermodynamic conditions for precipitation. Macroscopic experimental results at higher metal concentrations (>100 μM) with visible precipitates provide quasi-validation of the thermodynamic modeling. Oxidation of Fe(II) to Fe(III) is likely to be rapid in all in vitro OP assays, transforming Fe to a much less soluble form. Fe precipitates are likely to increase the rate of precipitation of other metals and possibly induce co-precipitation. These results have direct relevance for all PO₄-based assays; the implications for studies of PM toxicity are discussed.
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    Rapid Bacterial Detection and Identification of Bacterial Strains Using Machine Learning Methods Integrated With a Portable Multichannel Fluorometer
    (IEEE, 2023-08-09) Hasan, Md Sadique; Sundberg, Chad; Hasan, Hasibul; Kostov, Yordan; Ge, Xudong; Choa, Fow-Sen; Rao, Govind
    Rapid and sensitive bioburden detection is of paramount importance in different applications including public health, and food and water safety. To overcome the traditional limitations of bacterial detection i.e., lengthy culture time, and complicated procedure, a low-cost, portable multichannel fluorometer coupled with machine learning (ML) has been implemented in this study. Five different strains of bacterial samples were tested along with the negative control for time-series fluorescence data collection and analysis. We applied different conventional unsupervised and supervised machine learning techniques with extracted features followed by preprocessing of the data. Initially, machine learning algorithms were applied for the qualitative detection of bacteria by binary classification followed by regression analysis to predict the level of contamination for E. coli. The multiclass classification was used to identify gram-positive, and gram-negative bacterial strains and differentiate all the bacterial strains tested. Our results show that around 97.9% accuracy can be achieved for bacterial contamination detection for as low as 1 CFU/mL while 92.1% accuracy can be achieved for differentiating the gram-positive and gram-negative strains. Additionally, with 1 minute of data, high accuracy is obtained for detecting bioburden, proving the multichannel fluorometer’s rapid detection capability. The multichannel fluorometer integrated with ML analytics is capable of automating data analysis and determining accurate and rapid bacterial detection on-site with the prediction of bioburden levels and differentiating bacterial strains and the protocol can be applied to the biosensors with a similar data type.