Chemical Engineering Journal

This article presents the development of an advanced modeling and simulation platform for carbon capture systems, with a focus on integrated process analysis from upstream CO2 capture through to bioethanol production. The platform supports the evaluation of CO2 mitigation technology by coupling mathematical bioprocess models with an interactive desktop application. The biological system employs Chlorella vulgaris microalgae to fix CO2 through photosynthesis and generate carbohydrate substrates, which are subsequently converted to bioethanol by Saccharomyces cerevisiae yeast via fermentation. The simulation integrates three established kinetic models--the Monod, Logistic, and Luedeking-Piret models--to predict biomass growth, substrate consumption, and ethanol yield under varying operational conditions. A closed-loop CO2 recycling subsystem captures fermentation off-gases and reintroduces them into the bioreactor, enhancing overall carbon utilization efficiency. Three representative simulation scenarios demonstrated process efficiencies ranging from 1.09% to 93.78% of the theoretical maximum CO2-to-ethanol conversion efficiency, confirming the platforms capacity to evaluate a wide operational envelope. The Electron/React-based desktop application provides real-time visualization, interactive 3D bioreactor models, and a simulation history module, making it accessible to researchers, engineers, and students. The platform serves as a digital twin that bridges rigorous bioprocess mathematics with intuitive user interaction, providing a cost-effective tool for designing and optimizing sustainable carbon capture and biofuel production systems.

Source-separated organics (SSO) are widely processed via anaerobic digestion to produce biogas, yet alternative conversion pathways could generate higher-value products. Here, we demonstrate long-term continuous production and recovery of medium-chain carboxylic acids (MCCAs) from SSO via microbial chain elongation using a bench-scale anaerobic bioreactor operated for 911 days. The reactor was fed with SSO samples collected from two full-scale municipal organics processing facilities in Toronto, Canada, capturing facility-specific and seasonal variability in SSO composition. MCCA production depended strongly on the availability of lactate as an electron donor, which varied with SSO preprocessing operations and outdoor collection temperatures. To mitigate product inhibition, an in-line extraction system using hollow-fiber polydimethylsiloxane (PDMS, also known as silicone) membranes was integrated with the anaerobic membrane bioreactor, providing a robust and solvent-free alternative to solvent-based extraction methods. Maximum MCCA yields reached 0.31 g MCCA/ g VSfeed, with notable octanoic acid production (up to 20% of total MCCA), and production rates up to 0.84 g L-1 d-1. Acidification of the alkaline extract produced a phase-separated MCCA-rich oil ([~]95% purity) without addition of downstream separation steps. Microbial community analysis of the reactor revealed enrichment of putative chain-elongating bacteria, including Eubacterium and Pseudoramibacter species, while shifts in SSO feedstock microbiomes influenced substrate availability and product spectra. These results demonstrate the feasibility of sustained MCCA production from municipal organic waste streams and highlight opportunities to integrate chain elongation with existing anaerobic digestion infrastructure.

Current mechanical and chemical recycling strategies address less than 10% of global plastic waste, necessitating alternative valorization routes. Biological upcycling via enzymatic depolymerization combined with microbial conversion of the resulting monomers offers a promising pathway to transform mixed plastic waste into valuable alternatives. Here, we employed a single engineered Pseudomonas putida KT2440 for simultaneous co-utilization of five plastic monomers including ethylene glycol, terephthalic acid, adipic acid, 1,4-butanediol, and L-lactic acid, which can be derived from enzymatic hydrolysis of polyethylene terephthalate (PET), polybutylene adipate-co-terephthalate (PBAT), polyester-polyurethanes (PUs), and polylactic acid (PLA). Continuous fermentation over 21 days with alternating mixed-monomer feeds achieved steady state growth and complete substrate depletion, yielding adaptive mutations that informed iterative strain improvement. Further engineering enabled the biosynthesis of (R)-3-hydroxybutyrate (R-3HB), and 0.70 g L-1 R-3HB was produced directly from enzymatic hydrolysates of blended PET, PBAT, and TPU. These results establish a viable bio-based approach for upcycling realistic mixed plastics into value-added bioproducts.

Microbial fuel cells (MFCs) represent a promising technology for the simultaneous treatment of wastewater and bioelectricity generation. In this study, the MFCs are conceived as functional modules to be integrated into hydroponic cultivation systems, acting as a prosthetic rhizosphere capable of coupling wastewater treatment and bioelectrochemical activity with plant nutrition improvement. We compared the electrochemical performance of different microbial consortia comprising the electroactive bacterium Shewanella oneidensis, the plant growth promoting rhizobacterium (PGPR) Pseudomonas putida, and the plant biomass-degrading fungus Ophiostoma piceae, along with the supplementation with the quorum sensing (QS) analogue molecule 1{square} dodecanol. These microbial consortia are tested in MFCs fed with wastewater and root exudates to analyze enhanced feedstock assimilation, electricity production, and the generation of plant growth-promoting substances (PGPS). From an electrochemical perspective, we evaluated planktonic growth, anode adhesion, substrate consumption, and the production of redox-active molecules and PGPS such as flavins and siderophores respectively alongside key electrical production parameters, including current output and power. Among the different microbial configurations tested, the consortium combining S. oneidensis, P. putida, and O. piceae exhibited the highest electrical production potential. Moreover, within this framework, we detected the extracellular production of siderophores in MFCs containing P. putida, suggesting a potential role supporting hydroponic crop growth. Furthermore, the addition of 1-dodecanol led to an improvement of the bioelectrochemical parameters. These results highlight the potential of synthetic microbial consortia in MFC-based systems not only to enhance electricity generation from wastewater but also to provide added value in integrated hydroponic applications through rhizosphere-like functions.

Nanoplastics generated from plastic waste in our ecosystems are becoming increasingly prevalent as bulk plastics exposed to natural factors like water and sunlight fragment to the nanoscale over time. These incidental nanoplastics span a wide range of physicochemical properties, which makes studying nanoplastic interactions in biological systems difficult. Here, we characterized the behavior of incidental nanoplastics generated through mechanical abrasion within coacervate droplets to probe the surface properties of the nanoplastics. We used elastin-like polypeptides (ELPs) to create hydrophobic or charged coacervate microenvironments. Using optical microscopy and fluorescence quantification, we observed that nanoplastics made from polyethylene terephthalate (nPET), nylon 6 (nPA), and polystyrene (nPS) exhibited distinct partitioning behavior with more favorable interactions with hydrophobic droplets. This indicated that the hydrophobic polymer backbone was the predominate surface feature despite exposed functional groups of the incidental nanoplastics, in contrast to findings with model carboxylated latex nanospheres (nPS-COOH). Furthermore, the selective partitioning of incidental nanoplastics into the hydrophobic droplets was able to capture over 80% of nPET in solution, and after recovery of the protein droplet, was able to cumulatively capture over 75% of the nPET feedstock across multiple cycles. This work explores the nuanced surface characteristics of incidental nanoplastics, expands the application of coacervates as chemical probes, and demonstrates a biopolymer approach for effective nanoplastic removal.

Rapid quantification of sulfur-containing amino acids, particularly cysteine, in legumes is critical for assessing nutritional quality, supporting breeding program screening, and ensuring consistency in quality control processes. However, conventional methods, such as high-performance liquid chromatography (HPLC), are time-consuming and resource-intensive for high-throughput applications. This study evaluated artificial intelligence models for predicting cysteine concentration from surface-enhanced Raman spectroscopy (SERS) spectra of pea extracts. SERS spectra were acquired from 20 cultivars grown at three geographically distinct locations, with HPLC-measured cysteine concentrations as a ground truth reference. Linear regression, partial least squares regression, support vector regression, random forest regression, and a one-dimensional convolutional neural network (1D-CNN) were compared using within-cultivar splits and leave-one-cultivar-out (LOCO) evaluation. The 1D-CNN achieved RMSE 0.008 g/100 g within cultivars and maintained performance under LOCO, while other models showed limited generalization. Shapley Additive Explanations highlighted informative bands in the 630-760 cm-1 range, and noise modeling optimized scan-count selection.

Understanding how airborne particulates disrupt the human alveolar barrier requires in vitro systems that accurately replicate its composition and function. We present a biodegradable lung alveoli-on-a-chip that reproduces the architecture and physiology of the human air-blood interface using a porous poly(lactic-co-glycolic acid) (PLGA) membrane positioned between epithelium and endothelium under air-liquid interface (ALI) culture. The membrane, fabricated by porogen-assisted nonsolvent-induced phase separation, exhibited >50 % porosity, [~]2 {micro}m thickness, and mechanical compliance over 100-fold higher than conventional Transwell inserts, closely resembling the native interstitium. During co-culture, gradual PLGA degradation was compensated by cell-secreted extracellular-matrix (ECM) proteins such as collagen IV and laminin, forming a self-remodeling barrier that maintained integrity for at least 11 days. The platform supported stable epithelial-endothelial co-culture, high transepithelial electrical resistance, and physiologically relevant permeability. To demonstrate its utility, the chip was used to assess pulmonary toxicity of four types of waste-combustion-derived particulates, including rubber, plastic bags, plastic bottles, and textile fibers, delivered apically under ALI conditions. All combustion products reduced cell viability, increased hydrogen-peroxide release, and elevated {gamma}-H2AX expression, indicating oxidative and genotoxic stress, while disrupting barrier permeability. Rubber combustion particles elicited the most severe toxicity, causing the greatest loss of viability, accumulation of reactive oxygen species, and formation of DNA double-strand breaks. Together, these results establish a biodegradable, ECM-remodeling lung alveoli-on-a-chip as a physiologically relevant platform for investigating source-specific particulate toxicity and alveolar-barrier pathophysiology. By bridging environmental exposure models with human-relevant lung biology, this system provides a quantitative and translatable tool for evaluating respiratory risks and therapeutic interventions.

Microplastic (MP) pollution, which is present in the ecosystem in vast quantities, adversely affects human health and the environment, making it imperative to develop methods for its mitigation. The challenge of detecting or capturing MPs could potentially be addressed using plastic-binding peptides (PBPs). The ideal PBP for MP remediation would not only bind strongly to plastic, but also have other properties such as high solubility in water or great binding specificity to a certain plastic. However, the scarcity or absence of known PBPs for common plastics along with the lack of methods that can discover PBPs with all of the desired properties precludes the development of peptide-based MP remediation strategies. In this study, we discovered short linear PBPs with high predicted water solubility and binding specificity by employing an in-silico discovery pipeline that combines deep learning and biophysical modeling. First, a long short-term memory (LSTM) network was trained on biophysical modeling data to predict peptide affinity to plastic. High affinity peptides were generated by pairing the trained LSTM with a Monte Carlo tree search (MCTS) algorithm. Molecular dynamics (MD) simulations showed that the PBPs discovered for polyethylene, the most common plastic, had 15% lower binding free energy than PBPs obtained using biophysical modeling alone. PBPs with both high affinity and high predicted solubility in water were found by including the CamSol solubility score in the MCTS peptide scoring function, increasing the average solubility score from 0.2 to 0.9, while only minimally decreasing affinity for polyethylene. The framework also discovered peptides with high binding specificity between polystyrene and polyethylene, two major constituents of MP pollution, using a competitive MCTS approach that optimized the difference in affinity between the two plastics. MD simulations showed that competitive MCTS increased the binding specificity of PBPs for polystyrene and identified peptides with relatively great preference for either of the two plastics. The framework can readily be applied to design PBPs for other types of plastic. Overall, the high-affinity PBPs with desirable properties discovered by marrying artificial intelligence and biophysics can be valuable for remediating MP pollution and protecting the health of humans and the environment.

Liquid-liquid phase separation (LLPS) of biomacromolecules is a key mechanism driving the formation of membraneless organelles (MLOs) within cells, playing a crucial role in fundamental biological processes such as cell proliferation and stress response. Accurately understanding and predicting the phase separation propensity of proteins is essential for unraveling the assembly mechanisms of MLOs and their functions under both physiological and pathological conditions. Traditional research methods primarily rely on biochemical experiments, which are limited by low throughput, high cost, and difficulty in systematically exploring sequence-phase transition relationships. This study proposes and implements a novel three-stage, iterative paradigm based on artificial intelligence (AI) to propel phase separation research towards systematization, predictability, and mechanistic understanding. O_LIBenchmark Model Construction: A preliminary predictive model was established based on a Multilayer Perceptron (MLP) neural network, and the driving effect of phenylalanine/tyrosine (F/Y) residue-mediated {pi}-{pi} interactions on LLPS was validated. C_LIO_LIModel Robustness Enhancement: The model was optimized through adversarial training strategies, which effectively identified and eliminated misclassifications of "highly disordered non-phase-separating" trap sequences. This significantly improved the models generalization capability and reliability when handling complex, real-world sequences. C_LIO_LIPhysical Mechanism Integration and Functional Expansion: Incorporating the Uniform Manifold Approximation and Projection (UMAP) manifold learning method and constraints from non-equilibrium thermodynamics, a "fingerprint space" capable of characterizing the thermodynamic behavior of phase separation was constructed. This space enables cluster analysis of different MLO types, and the model can output a thermodynamic stability score for protein phase separation. Based on this score, we identified 10 high-confidence candidate proteins with the potential to form novel MLOs. The paradigm established in this study upgrades phase separation prediction from the traditional "binary classification" approach to a novel research framework characterized by "physical mechanism analysis + novel MLO discovery." It provides the phase separation field with a computational tool that combines high accuracy, strong robustness, and good physical interpretability. C_LI

Water-miscible metalworking fluids are widely used in industrial processes. Despite the fact that they contain biocides, they are almost always colonized by microorganisms, which degrade different components of the liquid, may clog machines due to biofilm formation and might pose a health risk to workers. In this study, samples from four metalworking machines operated with the same metalworking concentrate from two different locations, were analyzed with respect to microbial growth. Twenty-seven bacterial species and one fungus were identified. From these, twenty species were not observed before as colonizers of metalworking fluids. Growth of microorganisms, resulting health risks, putative contamination pathways and metabolic pathways involved in biodegradation are analyzed and discussed in this study. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/712622v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@16164e6org.highwire.dtl.DTLVardef@1273ee6org.highwire.dtl.DTLVardef@192aa20org.highwire.dtl.DTLVardef@1df4df2_HPS_FORMAT_FIGEXP M_FIG C_FIG

The scalable fabrication of stable colloidosomes with controlled permeability and defined multicompartmental architecture remains a critical challenge, limiting their broader use in molecular delivery and environmental remediation. Here, we develop a hybrid lipid-metal-organic framework (lipid-MOF) colloidosome assembled through an interfacial emulsification strategy that integrates the structural rigidity of ZIF-8 particles with lipid-mediated membrane stabilization. During assembly, ZIF-8 particles accumulate at the oil-water interface to form a shell, producing hollow micron-sized spherical colloidosomes. The resulting colloidosomes exhibit excellent colloidal stability in aqueous media for over 30 days with a zeta potential of approximately -50 mV. Nitrogen adsorption measurements reveal a surface area of 45 m2g-1 and an average pore width of 4 nm. Fluorescence imaging shows that hydrophobic Nile red preferentially partitions into the colloidosomal membrane, whereas hydrophilic fluorescein isothiocyanate (FITC) localize predominantly within the aqueous interior, enabling simultaneous encapsulation of molecules with contrasting polarity with loading efficiencies approaching 90%. Furthermore, the colloidosomes demonstrate rapid removal of model pollutants from water, achieving >90% removal of methylene blue and metal ions without stirring. Together, these results introduce lipid-MOF colloidosomes as a new class of hybrid platforms that unify structural stability, multicompartmental encapsulation, and efficient adsorption behavior, opening pathways toward sustainable platforms for drug delivery and environmental bioremediation.

Industrial waste containing hydrophobic pollutants, like oils and hydrocarbons, is toxic and difficult to degrade, posing both ecological and human health risks. Biosurfactants are eco-friendly surface-active compounds produced by microorganisms, known for their ability to lower surface and interfacial tension, enhancing the solubility and bioavailability of hydrophobic compounds, facilitating their breakdown. The current study focuses on isolating biosurfactant-producing bacteria from industrial waste sources near Dhaka, Bangladesh, and characterizing their properties, determining potential usage. Using diesel-enriched nutrient agar, bacterial strains were isolated and screened for biosurfactant production by oil displacement, emulsification index (E24%), and drop collapse assay. The most promising isolates were characterized according to their biochemical activities and 16S rRNA amplicon-based sequencing. Isolation and characterization of the surfactants have been carried out using chromatographic techniques. The identified bacteria passed the drop collapse and oil displacement tests. CTAB agar assay, indicates their anionic nature, showing an emulsification index ranging 10-41%. The potential biosurfactant producers belong to Bacillus, Pseudomonas, Acinetobacter, and Enterobacterium genera. The surfactants showed antibacterial, antifungal, and plant growth promotion activity and have been characterized in terms of pH stability, salinity, adhesion, and temperature tolerance. The study successfully identified and characterized potential biosurfactant-producing bacteria from industrial waste, highlighting their efficiency in breaking down hydrophobic pollutants and hydrocarbons. These microorganisms provide a green and economical substitute for synthetic surfactants due to their biodegradability and lower toxicity. Upon further research and scaling, these bacteria can be a good source of biosurfactants for potential applications in industrial, agricultural, and biomedical fields. IMPORTANCEThe study carries high significance as it creates multi-disciplinary scopes for utilizing these environmentally adapted biosurfactant-producing bacteria in industry, agriculture, and medicine. Since the bacterial isolates have hydrocarbon degradation ability, upon optimization for higher production, industrial usage in oil refinery and other industries can be adopted. Due to their biodegradable nature, usage in wound healing bandages and as antimicrobial agents in medicine will be noteworthy. The isolates have plant growth promotion ability and utilizing them as biofertilizer will reduce the dependency on chemical fertilizers. This is the first detailed report on biosurfactant-producing bacteria from this industrial waste-polluted Turag River of Dhaka City. Moreover, it compiles detailed screening protocols and methods for analyzing such environmentally friendly microbes. Such characterization also opens the scope for optimizing the production of the surfactant compounds on a large scale and utilizing them commercially.

BackgroundThe nasal microbiome is a collection of diverse microbial populations that inhabit the nose. Staphylococcus aureus is the most common opportunistic pathogen that colonizes the nasal mucosa, increasing the risk of invasive infections in immunocompromised and hospitalized patients. Clinicians usually prescribe antibiotics to decolonize the nasal cavities of at-risk patients from S. aureus. However, their broad antimicrobial activity can damage the resident nasal microbiome. Instead, naturally occurring compounds or resident bacteria in nasal microbiomes can effectively and safely exclude S. aureus from the nose. Cell culture and animal models have been used for nasal microbiome studies. However, their unstable microbiomes reduce the accuracy and reliability of the results. Recently, continuous bioreactors have been proposed as alternatives to these models. ResultsWe designed and operated a continuous bioreactor system to maintain stable nasal microbiomes. Next, we inoculated the bioreactor with nasal-swab specimens that we had collected from healthy volunteers. We operated the bioreactors under varying conditions (i.e., operating mode, dilution rate, temperature, pH, and medium composition), and determined the optimal conditions (continuous mode, 1 d-1, 30xlink:href=" pH 6.5, and synthetic nasal medium 3), resulting in stable microbiomes consisting of the main nasal bacterial species. The nasal microbiomes in the optimized bioreactors showed high reproducibility and resilience during a pH perturbation. Moreover, all microbiomes in the bioreactor, which were inoculated with six different nasal-swab specimens, maintained stable bacterial and metabolite compositions. In addition, we applied a synthetic microbial community (SynCom), which was derived from one of the volunteers, to demonstrate a S. aureus decolonization strategy. The bioreactor, inoculated with this SynCom, maintained a stable nasal microbiome for more than one month. Finally, different S. aureus strains that we inoculated in the SynCom showed distinct growth patterns within the otherwise stable community. ConclusionsThe continuous bioreactor enables the cultivation of stable nasal microbiomes for longer than one month by mimicking the environmental conditions of the human nose. The bioreactor is a valuable model for understanding the functions of the nasal microbiome and devising new decolonization strategies against S. aureus.

Detection of micro- and nanoplastics (MNPs) in human tissues has raised growing concern about their biological effects on tissue and cell function. While previous studies have examined MNP-cell interaction, most focused on limited cell and plastic types. Here, we present a comprehensive, quantitative investigation into how different types of nanoplastics (NPs) associate with and affect diverse cell types under physiologically relevant conditions. Using microfluidic-calibrated fluorescence microscopy, we quantify NP accumulation in cells in vitro and match cellular NP concentrations to levels reported in human tissues. While cell-associated NPs could be gradually released in vitro, they persist in vivo for over one month without detectable reduction in a mouse model. We discover that NP exposure at these levels broadly impairs cell proliferation across epithelial, endothelial, fibroblast, and immune cells, with cell type-dependent sensitivity. NP exposure also reduces motility in T cells and fibroblasts, with more complex effects observed in macrophages. Mechanistically, NP-cell association and trans-epithelial transport involved not only classical endocytic regulators but also pathways related to ion and water transport. Notably, NP association and release were highly sensitive to the extracellular fluid environment within the physiological range. By testing inhibitors of these pathways, we identified molecules that reduce NP-cell association and promote release. We further compared common NPs found in human samples and widely used in research: polystyrene (PS), polyethylene (PE), and polypropylene (PP). Although these NPs similarly impaired proliferation and motility, they showed markedly different cellular association and release dynamics. These findings reveal the impact of NPs on tissue cell functions and uncover novel regulatory pathways, establishing a quantitative framework for studying NP-cell interactions in biologically relevant conditions.

This study presents a multidisciplinary approach to evaluate the structure formation and digestion of lupin protein crosslinked with transglutaminase (TG). TG was applied at 0-10 U/g protein, and structural development was assessed by oscillatory rheology (G, G"), while SDS-PAGE and o-phthaldialdehyde (OPA) assays were used to evaluate protein participation and the reduction of free {varepsilon}-amino groups, respectively. Proteomics was further employed to characterise molecular features associated with crosslinking behaviour. Lupin protein showed a clear dose-dependent increase in gel strength during incubation, with G values reaching 214 {+/-} 43.9 Pa at 10 U/g TG, compared to 7.2 {+/-} 0.6 Pa in the untreated control. Across all conditions, G remained higher than G" throughout frequency sweeps, and low tan {delta} values confirmed the formation of elastic networks driven by covalent crosslinks. SDS-PAGE and OPA results consistently demonstrated efficient crosslink formation, which increased with both incubation time and TG dosage, with SDS-PAGE indicating involvement of specific protein fractions. Proteomic analysis revealed disordered structural domains in the protein are preferred regions to form crosslinks. Furthermore, TG treatment was found to slow the digestibility of the crosslinked lupin protein. Overall, this work demonstrates how integrating proteomic insights with functional measurements can guide the selection and optimisation of plant proteins for enzymatic structuring. The approach offers a rational pathway to enhance the functionality of alternative protein sources such as lupin, supporting the development of sustainable food systems, including applications in meat and dairy analogues.

Wastewater surveillance (WWS) is established as a vital tool for monitoring polio and SARS-CoV-2 with potential to improve surveillance for many other infectious diseases. This study evaluated the feasibility of detecting measles virus (MeV) RNA in wastewater as part of a national WS preparedness trial in Brisbane, Australia, from March to June 2025. Composite and passive sampling methods were employed in parallel at three wastewater treatment plants serving populations between 230,000 and 584,000. Nucleic acids were extracted and analyzed using RT-qPCR targeting MeV N and M genes to distinguish wild-type and vaccine strains. MeV RNA were detected in both 24-hour composite and passive samples on May 26 to 27, 2025 from the largest catchment of 584,000 which also included an international airport. No measles cases were reported in this city or region within 4 weeks of the WS detections. These were confirmed as vaccine-derived measles virus (MeVV) strain via specific RT-qPCR assay. Extraction recoveries varied (11.5% to 70.5%), with passive sampling showing higher efficiency. This is the first report of use of passive samples for detection of MeV. These findings are consistent with other studies reporting WWS results of both MeVV genotype A and wild type genotype B and/or D. It demonstrates the potential for sensitive MeV WWS with rapid differentiation of MeVV from wild type MeV shedding, including in airport transport hubs and with different sample types. Use of WWS could strengthen measles surveillance by enabling rapid detection of MeV RNA and supporting outbreak preparedness and response. This requires optimised methods which are specific to or differentiate wild-type MeV from MeVV. Furthermore, the successful detection of MeV using passive sampling in this study highlights its potential for deployment in diverse global contexts which may include non-sewered settings.

Formate is emerging as an important molecule in carbon capture and utilization technologies. However, its low electron density makes formate less attractive for energy storage. Some hydrogenotrophic methanogens can reduce formate to methane, thereby upgrading it into an established energy carrier. The bottleneck in this process is that 75% of the carbon is lost as carbon dioxide, and achieving a complete formate-to-methane conversion requires co-feeding hydrogen. However, hydrogen-dependent genetic regulation of formate metabolism inhibits simultaneous formate and hydrogen utilization in hydrogenotrophic methanogens. Here, we compared the catalytic performance of the genetically modified strain Methanothermobacter thermautotrophicus {Delta}H (pFdh) with M. thermautotrophicus Z-245 by conducting continuous cultivation at different hydrogen concentrations. While M. thermautotrophicus Z-245 is a natural formatotroph, M. thermautotrophicus {Delta}H (pFdh) was engineered to enable formate utilization via episomal expression of a formate dehydrogenase-gene cassette. We found that M. thermautotrophicus {Delta}H (pFdh) can simultaneously utilize formate and hydrogen. It continuously consumed formate at {approx} 0.1 mM dissolved hydrogen, enabling a 75.6% formate-to-methane conversion efficiency. M. thermautotrophicus Z-245 showed a declining formate consumption at {approx} 0.016 mM and only reached a maximum stable efficiency of 36.3%. These results suggest that M. thermautotrophicus {Delta}H (pFdh) is largely insensitive to hydrogen-induced genetic regulation; however, it still faces redox-related metabolic limitations at dissolved hydrogen concentrations above 0.4 mM. Overall, the findings reveal a potential strategy to circumvent hydrogen-induced regulation of formate metabolism and identify M. thermautotrophicus {Delta}H (pFdh) as a promising biocatalyst for formate-to-methane conversion.

PurposeLarge quantities of human pluripotent stem cells (hPSCs) are required for clinical applications. 3D suspension cultures are suitable for large scale manufacturing of hPSCs but yield, viability and quality are affected by the hydrodynamic environment. This paper characterizes the hydrodynamic environment inside vertical wheel bioreactors (VWBRs) as a function of size and agitation rates, measures its effect on cell aggregation and proliferation, and proposes the use of Lagrangian-based shear stress and energy dissipation rate (EDR) exposures to support scale-up. MethodsIn silico: Transient, 3D, turbulent flow simulations are conducted for two VWBR sizes (100, 500 mL) at five agitation rates between 20 and 80 rpm. Trajectories of cell aggregates of sizes from 200 to 1,000 microns are calculated, and shear stress and EDR exposures are collected along these trajectories. In vitro: ESI-017 hPSCs were cultured in VWBRs for 6 days. Aggregation efficiency and daily fold ratios were calculated based on cell counts and initial inoculation density. ResultsAggregate size, agitation rate and bioreactor size modulate cell aggregate exposures to EDR and shear stress, which significantly depart from maximum or volume average metrics used for scale-up. Combined in vitro/in silico results show EDR affects aggregation efficiency, cell counts and aggregate size, and has a small effect on daily fold ratios but a significant effect on total fold ratio. ConclusionHistory of trajectory-based cell aggregate exposures to EDRs provide a better scale-up basis for VWBRs than volume-averaged EDR. Shear stress does not significantly affect hPSC aggregation, proliferation and expansion in VWBRs under the tested conditions.

Neurotransmitters in the gut play a vital role in human health and neuroscience, and their real-time monitoring is essential for understanding underlying physiological mechanisms. However, bioelectronic systems capable of measuring neurotransmitters in vivo at the anatomical site of interest remain underdeveloped and largely depend on bulky, off-the-shelf electronic components, thereby constraining the development of systems that are both practical and minimally invasive. Here, we report a miniature ingestible pill that is capable of real-time in vivo sensing of two key neurotransmitters: serotonin (5-HT) and dopamine (DA). The system incorporates a fully printed three-electrode-based electrochemical sensor for neurotransmitter sensing and a custom application-specific integrated circuit (ASIC) that integrates all major functional blocks on a single chip, enabling a platform for fully wireless monitoring of gut neurotransmitters. The pill, measuring 5.8 mm in diameter and 19 mm in length, supports multiple electrochemical sensing techniques, including amperometry and voltammetry, with only 42 A of average current consumption. We demonstrate the ingestible platform through in vivo studies in rat animal models, enabling real-time monitoring of gut neurotransmitters.

Crop loss due to infection by pests and pathogens is a major barrier to the large-scale production of algal biofuels. Test systems have seen loss of green algae crops due to infection by the fungus-like Amoeboaphelidium occidentale FD01. While current antifungal compounds are effective in inhibiting the infection, their application raises the overall cost of the crop and lowers its economic viability as a biofuel source. Here we show that co-culturing environmentally harvested bacteria alongside algae crops can drastically lower the rate of infection in two different green algae species of interest for biofuel production. These bacteria-algae consortia increase the mean time to crop failure (MTTF) by up to 350% when tested under environmentally relevant conditions. While there was an increase in diversity over time, there was no statistically significant correlation between an increase in diversity and a longer MTTF. Community composition analysis reveals similarities between the bacterial genera growing alongside both green algae species even as bacterial harvest locations differed, although there was not a single dominant genus responsible for the increase in crop protection. These results show a promising new method of anti-fungal crop protection that can be applied to algal biofuels with no increase in fuel cost. HighlightsO_LIBacteria-algal cocultures protect against fungal pests without impact to productivity C_LIO_LIBacterial community composition is variable over time even as protection persists C_LIO_LIBacterial consortia can increase mean time to failure by 350% C_LI