Chemical Engineering Journal

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

Microplastic pollution from homopolymers such as polypropylene (PP), polystyrene (PS), and polyethylene (PE) poses growing health and environmental concerns, highlighting the need for robust polymer-degrading biocatalysts. We previously identified a naturally occurring cysteine-dependent hydrolase (CsdH) from Rhodococcus opacus, featuring a unique Cys160-Lys127-Asp39 catalytic triad and providing a novel mechanism for polyethylene terephthalate (PET) degradation. In this study, we have determined crystal structures of CsdH in complex with PP, PS, PE, and styrene and demonstrated the potential of the enzyme to directly degrade these homopolymers into their respective low molecular weight degradation products using LC-MS. Structural analysis reveals that the enzyme retains a conserved principal access tunnel while undergoing subtle substrate-dependent changes to accommodate different substrates. The enzyme exhibited broad plastic degradation activity with the highest weight loss observed for PE, followed by PP and PS. SEM analysis reveals surface roughness, cracks and grooves, confirming polymer degradation. Further, these results corroborate through FTIR and TGA analysis. These findings establish a strong foundation for enzyme engineering and aim at developing efficient microplastic degradation.

The unprecedented rise in greenhouse gases such as carbon dioxide (CO2), and their detrimental effects on the atmosphere have intensified the current climate emergency. This challenge has inspired the development of novel carbon capture and utilisation (CCU) technologies, specifically microbial electrosynthesis (MES), for the bioelectrochemical fixation of CO2 into commodity chemical compounds (CCCs) using microbial biocatalysts. In this study, we systematically enriched and maintained robust mixed microbial communities of electroactive bacteria (EAB) from wastewater treatment sludge to serve as biocatalysts for MES. The MES performance of these biocatalysts under different operational conditions in bioelectrochemical systems (BES) reactors were investigated. BES reactors with enriched biocatalysts incubated and maintained under controlled conditions produced higher CCCs yields than those developed at ambient temperatures, while strict anaerobic operations further improved the MES efficiency compared to aerobic conditions. At an applied potential of -1000 mV vs Ag/AgCl, CO2 conversion reached up to 88.11%, significantly outperforming the lower potential (-600 mV) operation. These findings underscore the importance of systematic development of robust and stable biocatalysts, as well as maintaining strict anaerobic conditions, for achieving efficient MES performance, thereby demonstrating the potential of our approach for scalable industrial CCU applications.

Bench-scale microbial fuel cells (MFCs) treating mining wastewater frequently exhibit operational variability and uncharacterized degradation that obscure true biocatalytic performance. To decouple genuine biological treatment effects from mechanical failures, this paper presents an integrated diagnostic framework validated on two bench-scale systems treating heavy-metal-rich gold mine tailings. The first system evaluates Micractinium inermum algal bio-augmentation (System 1), while the second compares Psychrobacter alimentarius- and Trichococcus patagoniensis-dominated anodic consortia (System 2). To overcome single-reactor constraints, the framework integrates paired time-series statistical modeling, an adaptive percentile-floor change-point detector, equivalent-circuit modeling, and baseline-corrected spectroscopy (XRD/FTIR). Applying the framework to these systems uncovers previously masked dynamics: statistical analysis demonstrates that algal biocatalysis provides no voltage advantage under stable operation (+0.17%) but increases output by 27.54% under diurnal perturbation, while periodicity analysis links these diurnal shifts to the chamber photoperiod. Furthermore, heavy-metal remediation (up to 97.7%) is governed by system-level physicochemical mechanisms rather than algal-specific processes. The change-point detector successfully isolates distinct failure modes, distinguishing a recoverable excursion from terminal structural collapse. Finally, equivalent-circuit modeling reveals that the superior power density of Trichococcus consortia is driven by combined improvements in internal resistance and open-circuit voltage. Ultimately, pairing statistical controls with automated fault detection resolves operational ambiguity, offering a scalable baseline for health monitoring in bio-electrochemical wastewater treatment.

Methyl 12-aminododecanoate (ADAME) is a key precursor for Nylon 12 synthesis and an attractive target for sustainable microbial production. However, efficient biosynthesis of ADAME requires the coordinated oxidation and transamination of methyl dodecanoate (DAME), and identifying compatible enzymes for this multistep pathway remains challenging. In this study, we applied a machine learning-based bioinformatic screening approach to identify alternative enzymes for DAME-to-ADAME bioconversion. Among 18 selected distantly related AlkB homologs, AlkBGp01 from Alcanivorax sp. P2S70 showed activity toward DAME, producing 12-hydroxydodecanoic acid methyl ester (HDAME) and 12-oxododecanoic acid methyl ester (ODAME) despite low sequence identity to Pseudomonas putida AlkB. Further screening identified compatible redox partners, AlkG60 from the same species as AlkBGp01 and AlkTp04, whose combination with AlkBGp01 resulted in almost sixfold higher ODAME production than that achieved with P. putida AlkBFGJLT. ODAME production was further increased by more than threefold by conjugating these three newly identified proteins. The optimized single-plasmid system comprising conjugated AlkBGp01, AlkG60, and AlkTp04, together with AlaD and newly identified omega-transaminases EAV41574 produced 0.28 mM gDCW-1 ADAME, representing a 5.6-fold improvement over the previously reported P. putida AlkBFGJLT-based system with AlaD-CV2025. These results demonstrate that machine learning-guided enzyme discovery can identify functional distantly related homologs and compatible enzyme combinations for constructing efficient synthetic pathways. This study also highlights the importance of optimizing pathway architecture, including enzyme conjugation and plasmid configuration, for improving microbial production of bio-based monomer precursors.

The growing concerns due to plastic pollution in India have intensified the search for sustainable materials. Mycelium-based composites (MBCs) have emerged as bio-based alternatives for packaging and thermal insulation applications. India, the worlds largest producer of bananas, generates significant quantities of banana biomass (~200 tons per hectare per year), much of which remains underutilized. The banana peduncle, the stalk that supports the fruit bunch, is one such underutilized biomass. In this study, banana peduncle fibers were used as the main substrate with Pleurotus ostreatus for the fabrication of MBCs. Banana peduncle fibers were mixed with wood shavings (10-50 wt%) to enhance the dimensional stability and structural integrity of the composites. The properties of developed MBCs such as density, shrinkage, moisture absorption, water absorption, morphology, compressive properties, and thermal conductivity were studied. The 90% banana peduncle fibers-10% wood shavings formulation showed the highest radial mycelial growth rate (7 mm/day). MBCs consisting of 100% banana peduncle fibers had volumetric shrinkage of 36%, while the incorporation of 30-50% wood shavings reduced shrinkage by approximately 17%. Among the formulations, MBCs containing 30% wood shavings had the highest compressive strength (4.82 MPa) and compressive modulus (1.78 MPa), whereas MBCs containing 50% wood shavings had the highest recovery (59.6%). In contrast, MBCs fabricated using 100% banana peduncle fibers had the lowest thermal conductivity (0.04 W/m. K). These results demonstrate that banana peduncle fibers are a promising lignocellulosic substrate for the development of MBCs for sustainable packaging and thermal insulation.

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-.

Micro/nanoplastics (MNPs) are increasingly recognized as a persistent environmental contaminant of concern, yet polyisobutylene (PIB), an industrially significant polymer used as industrial sealants, adhesives, cable insulation, lubricant additives, and chewing gum bases, remains poorly studied relative to more commonly studied plastics such as polyethylene terephthalate (PET), polystyrene (PS), and polyethylene (PE). This study investigated the multisystem toxicological effects of chronic dietary exposure to PIB-MNPs in Drosophila melanogaster. PIB-MNPs (> 5 micron) were synthesized via the solvent evaporation method and characterized using scanning electron microscopy (SEM) and Fourier Transform Infrared spectroscopy (FTIR), confirming successful synthesis. Flies were chronically exposed to PIB-MNPs (1, 2, or 3 mg) alongside an SDS control and a water control to 50 mg of yeast over 21 days, with toxicological outcomes assessed via survival analysis, climbing assays, fluorescence brain imaging, fertility/fecundity assays, the Smurf gut-permeability assay, Ellman's AChE activity assay, and RT-qPCR analysis of Stat92E expression, a marker of inflammation. Chronic exposure to PIB-MNPs reduced survival, with females exhibiting markedly greater susceptibility than males. Exposure progressively impaired locomotor performance and induced brain vacuolization suggestive of neurodegeneration, peaking at day 15. AChE activity declined in a concentration-dependent manner by day 21. Female fecundity, egg-hatching rate, and ovarian egg-chamber maturation were significantly reduced, while male gonadal cyst cell numbers remained largely unaffected. PIB-MNPs exposure also induced a positive Smurf phenotype, indicating compromised intestinal barrier integrity and upregulated Stat92E expression, consistent with activation of a systemic stress response. Notably, several endpoints showed non-monotonic, concentration-independent trends, suggesting particle aggregation may influence effective bioavailability. Collectively, these findings establish PIB-MNPs as a multi-system toxicant in Drosophila.

Fermentation is a process through which microorganisms are used to transform basic molecules into high value substances in various applications such as biopharma, food production, and biofuel generation. In such processes, monitoring the health of microbial cells is essential, as it is a key factor in the final product yield. Traditional methods of assessing the health of fermenter populations have involved manually extracting samples from the fermenter for benchtop testing, which lacked the accuracy and efficacy of real-time continuous measurements. This has opened the door for inline sensors to be integrated into fermenters for continuous measurement; including microfluidic based Electrochemical Impedance Spectroscopy (EIS). EIS utilizes a range of frequencies to gain insight into the characteristics of microbial populations, such as cell size and membrane capacitance. EIS cytometry, in particular, has emerged as a relevant tool, as it enables single-cell level detection in real-time. However, current EIS cytometry devices cannot be deployed directly within fermenters due to the high cell density conditions. This has limited EIS analysis in fermenters to bulk measurements averaged across a population. Here, we present a microfluidic chip that uses passive cell-flux-partitioning geometry to direct cells from high-density culture through a single-cell-constrained flow path containing integrated impedance-sensing electrodes. The proposed chip combines geometric channel design and embedded electrodes to enable continuous impedance measurements from cell-containing flow while operating at input cell densities on the scale of 10^9 CFU/mL. This work demonstrates a first step toward integrating single-cell-constrained microfluidic handling with impedance sensing for inline monitoring in industrial biomanufacturing.

Paper-based colorimetric sensors offer a low-cost and accessible platform for point-of-care (POC) analysis, but enzyme activity loss during coating and drying can weaken analytical signals and require high enzyme loadings or complex immobilization procedures. Although surfactants are widely used to improve wettability in paper-based assays, their potential contribution to colorimetric performance beyond these effects remains unclear. Here, we investigated surfactant-assisted colorimetric signal enhancement in a glucose assay implemented on a 96-puddle paper plate (96-PPP) and identified Tween 20 as the most effective surfactant. Its effect on detection performance became more pronounced as glucose oxidase (GOx) loading decreased; at 0.1 mg/mL GOx, Tween 20 lowered the limit of detection (LoD) from 0.113 to 0.034 mg/mL (approximately 3.3-fold) over a working range of 0-5 mg/mL, despite no statistically significant change in the measured contact angle at this loading. Tween 20 had no appreciable effect on the reaction in solution but preserved 95% of the apparent reaction rate constant after drying, compared with 11% without it, and atomic force microscopy (AFM) revealed a more dispersed dried enzyme morphology on mica. Tween 20-containing sensors also showed slower signal decay during repeated wetting-drying cycles and thermal stress, retained 77% (vs 26%) of the response at 400 mM NaCl, and exhibited within-PPP and between-batch coefficients of variation (CVs) below 10% (vs 12.3-19.5%), while maintaining glucose selectivity over potentially interfering molecules. These results indicate that Tween 20 enhances paper-based glucose sensing beyond wettability, in part by retaining enzyme cascade activity during drying, although the contributions of the individual enzymes and the underlying mechanism remain to be established.

Anaerobic digestion is a sustainable process for methane production that relies on complex microbial networks. While simplified enriched consortia offer a promising strategy to improve process control, excessive simplification can disrupt key functions and microbial partnerships, reducing community resilience. In this study, we investigated whether simplified methanogenic communities could re-diversify and maintain methane production when exposed to more complex substrates, namely butyrate and glucose. We also evaluated the effect of vitamin and amino acid supplementation on sustaining key methanogens and beneficial microbial partners. Three methanogenic communities were monitored over three months for methane production and microbial diversity while receiving butyrate and/or glucose, with different vitamin or amino acid supplements. Exposure to more complex substrates successfully restored the diversity of acidogenic and acetogenic populations, even after prolonged feeding with simple substrates, highlighting both the resilience of the simplified communities and the ecological importance of low-abundance taxa. However, the transition reduced process stability and methane production, likely due to substrate overloading. The results further suggest that substrate complexification should be introduced stepwise, promoting acetogenesis before acidogenesis. This fundamental study brings new light on which factors must be considered in the long-term goal of designing tailored-made consortia for anaerobic digestion.

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.

Valerolactam is a promising bio-based monomer for polyamide synthesis, but microbial production remains limited by inefficient 5-aminovalerate (5AVA) cyclization, by-product formation, and insufficient process optimization. In this study, Corynebacterium glutamicum was engineered for valerolactam production by expression of davBA from Pseudomonas putida and lysP from Escherichia coli, followed by conversion of 5AVA to valerolactam using the recently identified avaC gene from Collinsella intestinalis. Flask cultivations confirmed efficient valerolactam formation with only minor accumulation of the by-products L-lysine, 5AVA, and glutarate. Batch bioreactor experiments showed that higher glucose concentrations increased titers but also promoted by-product accumulation, whereas increasing the dissolved oxygen setpoint from 30% to 50% improved growth and volumetric productivity. Intracellular cofactor analysis revealed declining energy status and shifts in redox balance during production. Based on these findings, carbon-limited fed-batch cultivation at 50% rDO improved production performance, reaching 3.6 g/L valerolactam with a yield of 0.231 g/g and a volumetric productivity of 0.075 g/L/h, while minimizing by-product formation. These results establish AvaC-based C. glutamicum as a promising platform for sustainable valerolactam biosynthesis.

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

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.

Influenza A virus (IAV) defective interfering particles (DIPs) are regarded as promising broad-spectrum antivirals due to their high efficacy demonstrated in animal trials. Previously, an OP7 chimera DIP was generated that showed markedly enhanced antiviral efficacy compared to conventional IAV DIPs. In addition, a high-yield cell culture-based production process for OP7 chimera DIPs was established in perfusion mode. However, due to the technical complexity of this technology, we aimed to combine the advantages of perfusion in terms of yield with that of a simple and less complex batch process. In the present work, we established a fed-batch production process for OP7 chimera DIPs that allows a more straightforward implementation into existing production facilities. Using a feeding regime that was able to overcome the cell density effect, we achieved productive virus infection in stirred tank reactors until a cell density of 10E+6 cells/mL. Furthermore, we obtained a high virus titer of 3.8 log10(HAU/100 uL), corresponding to a 3.4-fold increase compared to a reference batch production. Moreover, the produced material demonstrated a significantly increased interfering bioactivity shown by an in vitro potency assay. Interestingly, this efficacy was similarly high as compared to OP7 chimera DIP material previously produced in perfusion mode, despite twofold lower virus titers. Finally, we demonstrate robustness of OP7 chimera DIP seed virus trains, which is significant step towards later pharmaceutical production. Further, corresponding GMP MDCK-PB2(sus) cell and OP7 chimera virus banks are now available for later biomanufacturing further use. In conclusion, the established fed-batch process offers a valid n additional option for pharmaceutical grade OP7 chimera DIP GMP manufacturing production under GMP conditions required for subsequent clinical testing and for later large-scale manufacturing.

Background: Hydroxyectoine is a high-value compatible solute with applications in cosmetics, healthcare, and biotechnology. Its microbial production has traditionally relied on halophilic organisms requiring high-salinity cultivation, motivating the development of non-halophilic production hosts. Corynebacterium glutamicum is an attractive platform because of its industrial robustness and high metabolic capacity through the aspartate-family amino acid pathway. Results: A synthetic hydroxyectoine pathway was constructed by combinatorially varying the predicted translation initiation rates of four translational units comprising ask, ectAB, ectC, and ectD. A library of C. glutamicum transformants was screened at 40{degrees}C using hydroxyectoine-associated thermoprotection as a functional selection principle. Fast-growing variants generally displayed increased hydroxyectoine formation and higher hydroxyectoine-to-ectoine ratios than a strain carrying the native pathway configuration. The selected combination reached a hydroxyectoine-to-ectoine ratio of 4.7 and was transferred into the lysine-producing strain DM1729SL in order to increase precursor availability and hydroxyectoine production. Carbon-limited fed-batch cultivations showed that both temperature and dissolved oxygen influenced production. The best performance was obtained at 30{degrees}C and 50% relative dissolved oxygen, yielding 12.9 g/L hydroxyectoine with a yield of 0.161 g/g glucose and a volumetric productivity of 0.26 g/L/h after glucose depletion. A subsequent hypoosmotic downshock increased extracellular hydroxyectoine recovery to 14.5 g/L. Conclusions: Thermal stress-based combinatorial pathway balancing enabled efficient de novo hydroxyectoine production in C. glutamicum. Integration of precursor-enhanced host metabolism, controlled oxygen supply, fed-batch cultivation, and osmotic downshock resulted in a substantial improvement over previously reported de novo hydroxyectoine production in this host and establishes C. glutamicum as a promising platform for further hydroxyectoine process development.