Chemical Engineering Journal

Power-to-Gas technologies are emerging as a key strategy to integrate surplus renewable electricity into energy systems, through the conversion of green hydrogen into methane. However, the practical implementation of biological in situ biomethanation is still constrained by operational and design requirements that are incompatible with most existing anaerobic digestion infrastructures. This study demonstrates a stable and efficient mesophilic (37{degrees}C) in situ biomethanation process driven by substrate-induced microbial selection rather than relying on continuous hydrogen supply. Anaerobic digesters co-digesting sewage sludge from a wastewater treatment plant with lipid-rich greases recovered from dairy wastewater developed a pre-adapted hydrogenotrophic consortium capable of effective CO2-H2 conversion under mesophilic conditions. Long-term operation confirmed the robustness and persistence of this microbial structure. Upon H2 addition, methane concentrations up to 82 % were achieved under atmospheric pressure, without biogas recirculation, with hydrogen-to-methane conversion efficiencies up to 90% and methane productivities of 1.64 NLCH4.L-1d-1. 16SrRNA-based microbial community analysis revealed that dairy grease co-digestion selectively enriched hydrogenotrophic methanogens, particularly Methanospirillum, together with syntrophic fatty-acid-degrading bacteria such as Syntrophomonas, promoting efficient interspecies hydrogen transfer. Importantly, the lipid co-substrate enabled the establishment and long-term stability of the hydrogenotrophic pathway independently of hydrogen availability, mitigating challenges associated with intermittent renewable energy supply. Overall, these findings challenge the common reliance on thermophilic conditions, continuous hydrogen input, pressurization, and gas recirculation in in situ biomethanation, demonstrating that substrate-driven microbial selection can replace conventional engineering requirements such as thermophilic operation or reactor modifications, providing a simpler and scalable strategy for mesophilic in situ biomethanation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/731101v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@c62086org.highwire.dtl.DTLVardef@1813ed6org.highwire.dtl.DTLVardef@4462bcorg.highwire.dtl.DTLVardef@1ae2cfb_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG Highlights- Lipid-assisted co-digestion promotes stable biogas and biomethane production - Dairy wastewater greases enable mesophilic in situ biomethanation - An enriched hydrogenotrophic methanogenic consortium yields >82% CH4 - 70-90% H2-to-CH4 conversion efficiency under mesophilic, unpressurized conditions - Substrate-driven microbial selection enables in situ biomethanation in WWTP digesters

The environmental impact of synthetic textiles has prompted the search for sustainable and biodegradable alternatives. This study correlates the growth conditions used to produce kombucha-derived cellulose non-woven mats with their mechanical performance as a function of post-processing. Systematically, the fermentation and growth parameters of the non-wovens, including inoculum density, carbon-source loading, temperature, and pH value were investigated. Thick, uniform non-wovens were obtained using mildly acidic conditions that balanced nutrient availability and growth rate, moderate inoculum and carbon loading at 30 {degrees}C. Next, we used uniaxial tensile testing and rheology to thoroughly compare the mechanical properties of two post-processing routes, lyophilization and oven-drying against the as-produced wet non-wovens. Overall, the lyophilized non-wovens displayed the highest ultimate tensile strength (14.36 {+/-}0.9 MPa) and elongation at break (24.54 {+/-}1.9%), which were statistically greater than the oven-dried (2.54 {+/-}0.3 MPa, 6.03 {+/-}0.8%) and the wet non-wovens (1.66 {+/-}0.3 MPa, 9.35 {+/-}2.8%). We conclude by performing a proof-of-concept recyclability experiment: we showed that kombucha-derived clothing could be enzymatically degraded and then re-manufactured into new nanofibers by electrospinning. Together, these results demonstrate a circular pathway encompassing the growth and processing of mechanically robust kombucha-derived cellulose non-wovens, as well as their biodegradation and re-manufacturing.

Microbial fuel cells (MFCs) have been widely investigated as decentralized bioelectrochemical systems capable of converting organic substrates into electricity. However, their long-term autonomous operation is constrained by substrate depletion in the anode compartment, leading to metabolic starvation of electroactive biofilms and a decline in power output. Conventional MFC design treats substrate crossover through the membrane separator as a parasitic loss that reduces coulombic efficiency. In this work, we propose a conceptual inversion of this paradigm by considering controlled cathodic-to-anodic substrate crossover as a passive mechanism to sustain basal microbial metabolism during periods of substrate scarcity. A transport-reaction framework is developed to quantify the balance between membrane-mediated substrate flux and microbial maintenance demand within the anode biofilm. Based on this balance, a dimensionless maintenance crossover Damkohler number (Dam) is introduced to define three operational regimes: starvation-dominated (Dam >> 1), balanced autonomous (Dam {approx} 1), and crossover-dominated (Dam << 1). The framework integrates membrane transport theory with biofilm kinetics to evaluate the effects of separator properties, substrate gradients, and current-dependent electro-osmotic transport on system stability. Order-of-magnitude analysis indicates that achievable crossover fluxes span several orders of magnitude depending on separator characteristics, suggesting that membrane properties critically influence system behavior. This perspective reframes substrate crossover from a loss mechanism to a potential design variable, offering a conceptual tool for enhancing resilience and guiding separator selection in MFCs intended for long-duration, and low-maintenance operation. HighlightsO_LIControlled crossover can sustain microbial metabolism in MFCs C_LIO_LIIntroduces maintenance crossover Damkohler number (Dam) C_LIO_LIIdentifies regimes for autonomous and starvation operation C_LIO_LILinks membrane properties to long-term system stability C_LIO_LIReframes crossover as a design variable, not only a loss C_LI

Methanogenic archaea, such as Methanothermobacter marburgensis, represent a powerful biological platform for carbon capture and valorization, directly converting carbon dioxide (CO2) and molecular hydrogen (H2) into proteinogenic amino acids (AAs). In this study, we present a controlled and scalable strategy for tailoring AA production (biosynthesis and secretion) in continuous gas fermentation. By applying various Design of Experiments (DOE) techniques, we systematically identified and optimized key process parameters governing AA biosynthesis and shaping a targeted AA secretion profile. A hybrid modeling framework combining experimental data with scale-independent parameters derived from computational fluid dynamics (CFD) enabled robust performance prediction across bioreactor scales. This model-driven approach successfully translated the process from 120 mL glass bottles via 2 L to 150 L reactors, corresponding to a reaction-volume scale-up factor of 2000. These findings set the foundation for a robust and predictive platform for sustainable AA production, positioning archaea as a high-potential alternative in industrial biotechnology.

Multi-stage continuous bioprocessing can increase volumetric productivity, operational consistency, and process throughput, but its design is complicated by coupling among dilution rate, reactor volume, feed allocation, and cellular physiology. Here, we present ContiDesigner, available at https://chemnettools.anc.univie.ac.at/ContiDesigner/, a mechanistic steady-state framework and interactive web tool for the system-level design of continuous fermentation cascades. Comparing one- and two-stage configurations at equal total reactor volume and outlet flow, ContiDesigner reveals how internal flow and reactor volume allocation shape space-time yield and identifies productivity-maximizing operating conditions. Compared with one-stage processes, two-stage cascades favor lower over-all dilution rates, thereby preserving residence time in the production stage. The first-stage dilution rate approaches the corresponding one-stage productivity optimum, but the cascade optimum occurs earlier, reflecting a system-level compromise between biomass generation and production-stage residence time. However, two-stage operation outperforms optimized one-stage operation only when non-growth-associated production in the second stage is sufficiently strong, whereas increasing growth coupling favors one-stage operation. Two case studies demonstrate both the potential and limits of process intensification. An optimized two-stage design is predicted to achieve a more than 1.5 fold increase in space-time yield for poly-R-3-hydroxybutyrate (PHB) production compared with a published experimental five-stage cascade, whereas the lactic acid case study identifies conditions under which staging offers no advantage. ContiDesigner translates these design principles into an accessible workflow to explore feasible operating regions and prioritize cascade designs for experimental evaluation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/743657v1_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@ef58faorg.highwire.dtl.DTLVardef@1ba88a4org.highwire.dtl.DTLVardef@160edd3org.highwire.dtl.DTLVardef@9dda34_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIContiDesigner enables system-level design of continuous fermentation cascades C_LIO_LIHigh stage-one dilution supports biomass generation C_LIO_LILow stage-two dilution preserves productive residence time C_LIO_LIYet two-stage cascades favor lower overall dilution than one-stage systems C_LIO_LITwo-stage advantage requires strong non-growth-associated production in stage two C_LI

Bioelectrochemical reduction of dilute nitrate (NO-; sub-mM to low-mM range) to ammonium (NH) offers a promising route toward circular nitrogen management from contaminated groundwater and environmental waters. However, on-site application of bioelectrochemical systems remains challenging due to low reduction rates and poor electron transfer efficiency of naturally formed biofilm electrodes. Here, we constructed a hydrogel biocathode by applying a carbon black/riboflavin/sodium alginate/cellulose hydrogel incorporating Shewanella oneidensis MR-1 cells to a graphite felt electrode via brush coating. The hydrogel electrode achieved NH production rates of 0.16-0.19 mol m-3 h-{superscript 1} without NO2- accumulation, and these rates were maintained without significant performance loss across three consecutive cycles with medium exchange over 1.5 days of total operation. The hydrogel electrode increased the current density by more than 5-fold compared with a conventional S. oneidensis biofilm electrode, indicating enhanced electron transfer efficiency per unit biomass, which directly contributed to the high NH production rates. The electricity consumption for NH production of 1.68-2.39 x 10{superscript 2} kJ g-N-{superscript 1} was substantially lower than that of metal catalyst systems at comparable NO- concentrations (typically, >104 kJ g-N-{superscript 1}). These findings demonstrate that the hydrogel electrode design represents an energy-efficient, and readily fabricated platform for bioelectrochemical NH production from dilute NO-.

Polycyclic aromatic hydrocarbons (PAHs) are hazardous organic contaminants for which microbial bioaugmentation is a promising remediation strategy, but poor persistence of introduced microorganisms can limit efficacy. Encapsulation may improve persistence, yet the influence of capsule design, microbial species, and environmental conditions on performance remains poorly understood. We evaluated alginate encapsulation of the PAH-degrading bacteria Pseudomonas putida and Novosphingobium aromaticivorans across nutrient conditions and capsule formulations. Encapsulation effects varied by species and medium, influencing growth rate, maximum cell density, overall growth, and lag time; notably, encapsulation shortened lag time of N. aromaticivorans in sRB15 medium (36.9 h to 3.9-5.3 h). Enumeration methods also affected apparent cell recovery. After 8 weeks, encapsulation had no significant effect on P. putida but resulted in increased concentrations of N. aromaticivorans relative to planktonic cultures (1.22 x 10; vs. 2.05 x 10; CFU/mL). Capsule composition further influenced cell retention: increasing alginate approximately doubled capsule-associated cell concentrations, while chitosan coatings reduced cell concentrations within capsules without affecting external concentrations. These findings demonstrate that the benefits of encapsulation are species- and environment-dependent and that capsule formulation can be tuned to influence bacterial persistence and release, informing the design of encapsulated inoculants for bioaugmentation applications.

The 2020 SARS-CoV-2 pandemic renewed global interest in wastewater-based epidemiology (WBE) as a tool for monitoring public health. Molecular analyses of wastewater are often limited by the small volumes of wastewater that can be processed, due to column clogging, handling constraints, and processing time. Additionally, inhibitors in the complex wastewater matrix reduce the sensitivity of downstream assays such as RT-PCR and sequencing. To address these limitations, we developed a novel column by incorporating a hydrophobic pre-filtration layer and sequential glass fiber filters. This enhanced column design, PureBioX Xpurify Column, enables processing of 1.58 times more wastewater (a 58% increase in throughput) while reducing RT-PCR inhibitors and maintaining compatibility with existing workflows. Despite a modest reduction in nucleic acid yield, the modified column consistently improved viral RNA detection sensitivity, including for SARS-CoV-2. This accessible, scalable upgrade strengthens the utility of direct capture methods in WBE-based public health surveillance.

Accurate and decentralized quantification of serotonin, also known as 5-hydroxytryptamine (5-HT), in biological fluids is critically important for the diagnosis, prognosis, and therapeutic monitoring of neurological and psychiatric disorders. However, conventional analytical methods generally rely on centralized laboratory infrastructure, skilled personnel, and labor-intensive sample processing, which restrict their applicability in rapid near-patient and point-of-care settings. Herein, we report a portable molecularly imprinted polymer (MIP)-based electrochemical sensing platform for selective and on-site detection of serotonin using screen-printed carbon electrodes (SPCEs). The biomimetic recognition interface was fabricated through direct electropolymerization of a polydopamine recognition layer in the presence of serotonin as the template molecule, followed by template extraction to generate complementary recognition cavities for selective rebinding. The sensor fabrication parameters, including monomer concentration, electropolymerization cycles, template-to-monomer stoichiometry, and electrolyte pH, were systematically optimized to achieve improved sensitivity, selectivity, and signal stability. Under optimized conditions, the MIP/SPCE sensor exhibited a broad linear response from 10 pM -10 uM in phosphate buffer, with a correlation coefficient of R2 = 0.974 and an ultralow limit of detection of 0.16 pM. The analytical applicability of the platform was further validated in spiked artificial serum, where the sensor achieved an LOD of 0.12 pM, satisfactory recovery values of 88.66-96.02%, and acceptable precision with RSD values [&le;] 8.43% (n=3), confirming its reliability in a complex biological matrix. The developed sensor demonstrated excellent selectivity toward serotonin against physiologically relevant interferents, maintaining signal retention between 99% and 101%. In addition, the platform showed high operational repeatability with an RSD of 0.45%, good inter-electrode reproducibility with an RSD of 6.3%, and long-term storage stability, retaining 90-110% of its initial response over 28 days. Importantly, cross-platform validation using a smartphone-coupled potentiostat demonstrated strong analytical agreement with laboratory-grade instrumentation, as evidenced by R2 = 0.9967 and a slope of 1.023. These findings establish the proposed MIP/SPCE platform as a simple, low-cost, portable, and smartphone-compatible electrochemical device for field-deployable serotonin monitoring in clinically relevant samples.

The suspension culture of pluripotent stem (PS) cells in stirred bioreactors poses a delicate balance between maintaining homogeneous cell dispersion and avoiding excessive shear stress that can compromise cell viability and pluripotency. In this study, we used computational fluid dynamics (CFD) coupled with a discrete particle method (DPM) to simulate iPS cell behavior in a 5 mL delta-impeller stirred tank. Our analysis revealed that upward flow at the tank bottom and downward flow at the top are critical for maintaining a stable suspension. To optimize the stirring protocol, we applied Bayesian optimization to identify a time-dependent stirring schedule that begins with a high-speed phase for resuspension, followed by a low-speed phase for sustained suspension with minimal hydrodynamic stress. The optimized schedule demonstrated improved suspension ratio and reduced slip velocity, indicating lower mechanical stress on cells. These findings provide engineering insights into scalable bioreactor operation, contributing to the design of robust iPS cell manufacturing systems.

Analytical technologies that can provide quick, precise, and continuous information regarding process performance are necessary for the development of biopharmaceutical manufacturing. Conventional bioprocess monitoring is largely dependent on laboratory-based data and offline sampling, which can restrict process management and cause delays in decision-making. This study develops a machine learning-enabled Raman spectroscopy framework for Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) applications in bioprocess manufacturing. Five predictive modeling techniques--Partial Least Squares (PLS) regression, Support Vector Regression (SVR), Random Forest, Extreme Gradient Boosting (XGBoost), and Neural Networks--were used to analyze Raman spectral data from an Escherichia coli fermentation dataset. The models were assessed using the coefficient of determination (R{superscript 2}), root mean square error (RMSE), and mean absolute error (MAE) to predict two crucial fermentation parameters: the concentrations of glucose and acetate. The superior performance of PLS regression for glucose prediction and the improved prediction accuracy of XGBoost for acetate concentration demonstrated the importance of selecting modeling techniques based on biological complexity. Explainable artificial intelligence using SHAP analysis was incorporated to improve model transparency by identifying Raman spectral regions contributing to predictions. The suggested architecture shows how Raman spectroscopy and machine learning can be combined to assist automated process monitoring, enhance process comprehension, and hasten the implementation of real-time quality judgments in next-generation biomanufacturing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/740653v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@6a460aorg.highwire.dtl.DTLVardef@11c68e5org.highwire.dtl.DTLVardef@2acf7aorg.highwire.dtl.DTLVardef@9b890a_HPS_FORMAT_FIGEXP M_FIG C_FIG Overall workflow of the Raman spectroscopy-based machine learning framework for PAT and RTRT implementation. Raman spectra collected from E. coli fermentation were preprocessed and analyzed using multiple machine learning algorithms for the prediction of glucose and acetate concentrations. Model performance evaluation and SHAP-based explainable AI analysis enabled the identification of important spectral features for real-time bioprocess monitoring. HighlightsO_LIDeveloped a Raman spectroscopy-based machine learning framework for real-time monitoring of critical bioprocess parameters. C_LIO_LICompared traditional chemometric modeling (PLS regression) with advanced machine learning approaches, including SVR, Random Forest, XGBoost, and neural networks. C_LIO_LIShowed that the biochemical target affects the models performance, with XGBoost improving acetate prediction and PLS offering better glucose prediction. C_LIO_LIIntegrated explainable artificial intelligence to identify Raman spectral regions contributing to bioprocess predictions. C_LIO_LIEstablished a pathway toward interpretable Raman-based Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) implementation. C_LI

The red palm weevil, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae), is a globally invasive quarantine pest threatening palm cultivation across 49 countries and inflicting annual economic losses estimated at over USD 100 million. Weevil larvae burrow into palm trunks, causing progressive internal structural damage that rarely produces visible external symptoms until lethal injury has occurred, rendering early detection exceptionally challenging. In the absence of effective early-warning surveillance technologies, tens of thousands of infested palm trees have been removed across major palm-cultivation regions in the Middle East and Mediterranean basin. Rapid, sensitive detection of volatile organic compounds (VOCs) emitted by weevil colonies and infested palm trees therefore represents a critical unmet need for timely pest surveillance and intervention. Existing artificial gas sensors lack the chemical selectivity required to discriminate among structurally similar VOCs, and no validated field-deployable early-detection platform has been established to date. Here, we report a portable biohybrid sensor array that mimics insect olfaction by exploiting two classes of diagnostic chemical signatures: the male-released aggregation pheromone (4RS,5RS)-4-methylnonan-5-ol (ferrugineol) and ethyl ester volatile blends emitted by weevil-infested palm trees. The R. ferrugineus odorant receptor RferOR1 was stabilised in lipid nanodiscs and co-immobilised with two in vivo-synthesised odorant-binding proteins (RferOBP1768 and RferOBP23) on quartz crystal microbalance (QCM) transducers to construct the biohybrid sensing platform. The sensor array achieved selective detection of airborne ferrugineol at a limit of detection of approximately 60 parts per billion (ppb) under field conditions, distinguishing infested from healthy palms. OBP- and OR-functionalised sensors retained full functional activity for 12 and 7 months, respectively, under ambient storage, confirming operational robustness and shelf life suitable for long-term field deployment. This work translates the molecular architecture of the insect olfactory system into a practical, field-validated chemical sensor platform with direct applicability to early-stage R. ferrugineus infestation monitoring and sustainable integrated pest management. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/744605v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@69f81forg.highwire.dtl.DTLVardef@120e05dorg.highwire.dtl.DTLVardef@16a0cb6org.highwire.dtl.DTLVardef@16889a8_HPS_FORMAT_FIGEXP M_FIG C_FIG

Global demand for platform chemicals and biomaterials urges us to seek sustainable strategies along with waste valorization to produce lactic acid (LA) sustainably. The study has designed a one-pot fermentation strategy by employing in-house produced ligninolytic and saccharifying enzymes on rice straw along with a consortium of hexose and pentose sugar co-fermenting microorganisms. Biological pretreatment with in-house ligninolytic enzyme was selected for the one-pot strategy from a comparison study of chemical and enzymatic pretreatment of rice straw. In this study, simultaneous pretreatment and saccharification of rice straw followed by LA fermentation by Lactobacillus casei- Lactobacillus rhamnosus system (35.58{+/-}0.29 g/L) was found out to be more efficient than Lactobacillus casei-Lactobacillus pentosus system (29.80{+/-}0.92 g/L). Thus, the L. casei- L. rhamnosus system (CR system) was selected and was further statistically optimized by response surface methodology (RSM) to yield 64.96 g/L of LA. The fermentation broth was decolorized and purified by ion exchange chromatography to yield 85.56% pure LA with 84.95% optical purity. The one-pot fermentation strategy has reduced the number of unit operations involved to synthesize LA from rice straw without compromising the yield and purity through a greener route. The use of in-house enzymes and consortium of lactic acid producing bacteria in one-pot presents a strategic approach to sustainable LA production. The biological enroute and the minimum use of chemicals during upstream, fermentation, and downstream processing adds to the carbon credit of the process. HighlightsO_LILactic acid was produced from rice straw using one-pot co-fermentation strategy C_LIO_LIUpstream processing employed in-house enzymes from fungal solid-state fermentation C_LIO_LIThe process addresses the underutilization of pentose sugars after saccharification C_LIO_LIA consortium LAB produced 64.96 g/L LA with 0.855 g/L.h productivity C_LIO_LIDownstream processing yielded LA with 85.56% purity and 84.95% optical purity C_LI

BackgroundContinuous glucose monitoring (CGM) systems are used not only for retrospective assessment of the glycemic profile but also for real-time decision-making, including automated insulin delivery. Accordingly, CGM performance characterization must capture not only the agreement of individual paired values but also the systems ability to reproduce the direction, rate, amplitude, and shape of glucose concentration change. Summary metrics, most notably MARD, cannot establish whether an observed deviation reflects an error in the formation of the test profile itself, a constant sensor offset, amplitude compression, a change in response rate, temporal misalignment, or hysteresis. ObjectiveTo adapt a programmable flow-based in vitro platform for the separate assessment of the experimentally delivered glucose profile and the dynamic response of CGM systems, and to propose a set of metrics that decomposes dynamic error into its components. MethodsGLU profiles were generated by programmable mixing of solutions at a constant total flow rate of 2 mL/min. Actual GLU concentration was independently measured with a SUPER GL2 glucose analyzer. Four static levels, three repeats of a 5.5[-&gt;]12.0[-&gt;]5.5 mmol/L profile, three repeats of a 6.0[-&gt;]3.0[-&gt;]6.0 mmol/L hypoglycemic profile, three 5.0[-&gt;]15.0[-&gt;]5.0 mmol/L profiles at different rates, one complex 4[-&gt;]18[-&gt;]3[-&gt;]12[-&gt;]5.5 mmol/L profile, and two proof-of-concept sensor experiments at 100- and 200-min transitions were investigated. Dynamic response was characterized by bias, MAE, RMSE, MARD, amplitude transfer coefficient K_A, rate transfer coefficients K_up and K_down, normalized shape RMSE, residual shift, and hysteresis loop area. ResultsAt the static levels, measured GLU exceeded the programmed value by 0.234-0.780 mmol/L. In the repeated 5.5[-&gt;]12.0[-&gt;]5.5 profiles, the ratio of actual to programmed rate was 0.978-1.083 on the rising phase and 0.987-1.157 on the falling phase, while the amplitude transfer coefficient was 0.967-1.066. In the hypoglycemic profile, minimum GLU was 2.55- 2.96 mmol/L, and time below 3.0 mmol/L was 15.2-72.6 min. The measured rates of 0.0519, 0.1045, and 0.2027 mmol/L/min preserved the intended ratio of approximately 1:2:4. In the complex profile, the programmed plateau of 18 mmol/L was not reached: mean measured GLU was 16.20 mmol/L. For CGM-A, K_A was 0.682 and 0.650, and K_up/K_down were 0.666/0.730 and 0.634/0.626; the corresponding values for CGM-B were 1.228 and 1.128, and 1.564/1.328 and 1.276/1.145. Hysteresis loop area differed 5- to 10-fold between the two sensor responses, exceeding an order of magnitude at the 100-min transition. ConclusionThe programmed concentration should be treated as a control setpoint, rather than as a reference measurement. The "programmed trajectory -- measured glucose -- CGM output" cascade first allows quantitative assessment of the agreement between the programmed and actually realized profile and only then separate characterization of sensor response. Decomposition of dynamic error into amplitude, rate, shape, and hysteresis components reveals differences that a single MARD value or correlation coefficient cannot capture.

Nile Red is widely used for the detection of microplastics because its fluorescence emission is sensitive to local polarity and can distinguish hydrophobic plastics from hydrophilic ones. The fluorescence of the molecular rotor, 9-(dicyanovinyl)-julolidine (DCVJ) is less sensitive to polarity but more to viscosity. DCVJ is less widely used for microplastic analysis, although it has been used to detect polystyrene nanobeads. Here, we compared these dyes with standard samples from the Hawaii Pacific University Polymer Kit 1.0 and confirmed that Nile Red, in general, was better for the detection and identification of microplastics. Fluorescence emission was analyzed using photography, as well as spectroscopy. The color and peak emission wavelength of some stained environmental microplastics were affected by additives. Raman spectroscopy was used to confirm the chemical identity of such samples. Although DCVJ emits green fluorescence on binding to some microplastics, a peak at 620 nm has been reported with polystyrene nanobeads, attributed to dimer/excimer formation. We confirmed this property and directly observed diffraction-limited spots using fluorescence microscopy, attributed to single or just a few nanobeads. Nile Red also stains polystyrene nanobeads and gave stronger signals than with DCVJ, but Nile Red was prone to false positives due to dye aggregation in aqueous solutions.

Layered double hydroxides (LDHs) are increasingly explored for agricultural, environmental, and biodelivery applications, but their composition-dependent effects on mammalian cells remain insufficiently defined. Here, we synthesized Al-Ni, Al-Co, and Al-Cu LDH nanoparticles and evaluated their physicochemical properties and biological responses across exposure-relevant mammalian cell models. The formulations showed hydrodynamic diameters of approximately 200-300 nm, moderate dispersity, strongly positive surface charge, and characteristic lamellar LDH features. Cytotoxicity was assessed using MTT, Calcein-AM, and Hoechst-PI assays in HaCaT, A549, and HT-29 cells, representing dermal, pulmonary, and intestinal exposure contexts, together with NIH/3T3 fibroblasts as a sensitive comparative model. LDH toxicity was strongly dependent on metal composition and cell type, with an overall trend of Al-Cu > Al-Co > Al-Ni and more pronounced cytotoxic effects in A549 and HT-29 cells. To detect cellular perturbations beyond overt viability loss, we applied high-content imaging using Live Cell Painting. Multiparametric single-cell profiling revealed composition- and dose-dependent alterations in acidic vesicle organization, nuclear texture, and cytoplasmic granularity. Notably, phenotypic deviations were detected at concentrations below those producing measurable effects in conventional viability assays, and linear discriminant analysis separated the phenotypic signatures induced by the three LDH formulations. Together, these findings show that LDH biological activity cannot be generalized across metal compositions and that high-content phenotypic profiling provides added sensitivity for detecting early cellular perturbations. This integrated approach supports composition-aware nanosafety evaluation and may inform the safer development of LDH-based technologies for agricultural and biotechnological applications.

Trace elements, such as iron, nickel, and cobalt are known to regulate methanogenic activity in anaerobic digestors used for waste valorisation, but the potential role of rare earth elements remains poorly understood. This study investigated the effects of lanthanum (La) supplementation on biogas production, methane generation, volatile fatty acid (VFA) formation, and carbohydrate utilisation in anaerobic digestion (AD). Biomethane potential (BMP) assays conducted under mesophilic conditions (37C) using methanogenic sludge granules, and glucose as substrate, were supplemented with 0.1, 1, 10, and 100 mg/L lanthanum chloride (LaCl3). Biogas production and composition was monitored over a 96-h incubation, while sacrificial, batch bioreactors were used to evaluate temporal VFA and carbohydrate profiles. La supplementation significantly enhanced biogas and methane production in a concentration-dependent manner. The highest cumulative biogas yield (478.9 mL, corresponding to 179.5 mL biogas/g COD) and methane production (285.7 mL, corresponding to 107.1 mL CH4/g COD) were observed with 100 mg/L LaCl3, corresponding to increases of 88.7% and 186%, respectively, compared with La-free controls. CO2 production also increased with La concentration, whereas hydrogen production remained comparatively low. Acetic and butyric acids represented the dominant fermentation products (80-88% of total VFAs), but profiles of accumulated VFA in the bioreactors diversified with La addition, including showing caproate production, indicating changed biodegradation dynamics in the methanogenic microbiome. These findings demonstrate that lanthanum can stimulate anaerobic digestion performance and methane generation, highlighting the potential as a novel trace element additive to enhance biogas production. Research is now required to elucidate the underlying microbial and biochemical mechanisms, and establish optimal dosing strategies for large-scale applications.

Given the current global environmental challenges, waste biomass is an attractive renewable resource for circular economies. Gasification of biomass yields syngas (CO, CO2, and H2) that is a suitable feedstock for gas fermentation in biomanufacturing of fuels and chemicals using acetogen microbes. While it is generally known that syngas composition influences both acetogen growth and process performance, we are lacking a consistent dataset quantifying these effects under controlled fermentation conditions. Here, we mapped the metabolic response of the model-acetogen Clostridium autoethanogenum to seven synthetic syngas mixtures during exponential batch growth in bioreactor fermentations. Notably, distinct gas compositions resulted in different fermentation profiles, affecting both growth and metabolite production. Maximum specific growth rates ranged within 0.05 0.13 h-1, with slower growth for low-CO mixtures. While acetate and ethanol production yields varied between 20-133 and 76-353 mmol per gram dry cell weight, respectively, minor production of 2,3-butanediol was detected. All syngas mixtures supported co-utilization of CO and H2, though gas uptake stoichiometry only moderately correlated with syngas content. Importantly, gas uptake stoichiometry strongly influenced carbon partitioning, with higher relative H2 uptake reducing CO2 loss or even realizing CO2 fixation together with increasing carbon flow towards metabolites. Interestingly, higher syngas H2 content favored ethanol and 2,3-butanediol production, while higher H2:CO uptake ratios increased total flux through the Wood-Ljungdahl pathway rather than selectively favoring reduced by-products. Our results are valuable for a better understanding of syngas composition effects on the acetogen biocatalyst and for process engineering towards optimizing gas fermentation performance. HighlightsO_LISyngas composition affects acetogen growth, gas uptake, and carbon distribution C_LIO_LIHigher H2:CO uptake ratios increase carbon flow through the Wood-Ljungdahl pathway C_LIO_LIHigher relative H2 uptake reduces CO2 loss and increases metabolite production C_LI

BackgroundMicrobial chain-elongation by Clostridium kluyveri using the products (acetate and ethanol) derived from the electrocatalytic CO2 reduction reaction (CO2RR) represents a unique sustainable strategy for producing C4-C6 chemicals from CO2. However, direct integration of electrocatalytic effluents with anaerobic bioprocesses is often impeded by the physiological incompatibility between electrocatalytic product streams and microbial metabolism. Specifically, CO2RR effluents commonly contain formate, which cannot be utilized by C. kluyveri for chain elongation and therefore reduces the overall carbon efficiency of CO2 conversion to C4-C6 chemicals. Moreover, both formate and the elevated phosphate concentrations typical of electrochemical reaction solutions may inhibit microbial growth. ResultsWe show that formate at concentrations of up to 50 mM did not inhibit the growth of or the chain elongation by C. kluyveri. Based on this finding, we developed a modular two-step bioprocess. In the first step, the acetogen Clostridium ljungdahlii converts formate in CO2RR product mixtures into acetate, thereby generating additional substrates for second-step C. kluyveri-driven chain elongation, thus increasing the CO2RR carbon-conversion efficiency to C- C6 chemicals. To address the issue of C. ljungdahliis inhibition by high phosphate concentrations in electrocatalytic solutions, we explored the use of C. ljungdahlii biofilms for the first, i.e. the formate-conversion, step. C. ljungdahlii biofilms exhibit tolerance to concentrated electrolytes, enabling the conversion of up to 50 mM formate in CO2RR solutions. ConclusionsThe demonstrated two-step process constitutes the basis for the development of a robust and carbon-efficient biological process for the scalable upgrading of C1-C2 CO2RR products into higher-value C4-C6 chemicals.

Rapid, selective detection of bacterial pathogens remains a central challenge. Here we report a label-free electrochemical biosensing approach that leverages protein-protein interaction (PPI)-derived peptides as recognition elements for rapid detection of Listeria monocytogenes (LM). The sensor design is inspired by the interaction between the LM virulence factor Internalin A (InlA) and the human host receptor E-cadherin (E-Cad1). Peptides derived from the InlA-binding domain of E-Cad1 were engineered as molecular recognition elements, with the E-Cad1(15-24) peptide displaying micromolar affinity and selective binding towards LM. Immobilization of these peptides on gold electrodes enabled bacterial detection by electrochemical impedance spectroscopy within 10 minutes, without labels or external signal amplification. A low peptide surface density was associated with enhanced binding-site accessibility and may facilitate multivalent interactions between the bacterial surface and the immobilized peptides. The platform produced a detectable response at experimentally tested concentrations as low as 1 CFU mL {superscript 1} and exhibited excellent selectivity under the conditions examined. This work introduces a chemically programmable, PPI-inspired biosensing paradigm that uses a reductionist approach and could potentially be extended to other pathogen targets.