Batch Cell Research Articles

Bridging activity gaps between batch and flow reactor configurations in the electroreduction of carbon dioxide.

To date, the understanding of various modes of CO2 mass transport remains incomplete, impeding the transfer of catalysts identified in the more accessible electrochemical batch cells to high-performance flow cells. In this work, we demonstrate that the meniscus region formed between the electrode and the convex liquid level due to the electrowetting of the catalyst plays a vital role in the CO2RR in batch cells. CO2RR in the meniscus region in batch cells exhibits similar performance with that in flow cells, and the performance disparity between these two configurations largely disappears when conducting CO2RR primarily in the meniscus region. An assembled double-sided gas diffusion electrode with a gas channel is developed to maximize the meniscus-like region, achieving a CO2RR partial current density of 640 mA/cm2geo on commercial Cu in the KHCO3 electrolyte. This performance represents the highest CO2RR activity in neutral buffered media.

Electrooxidative C(sp3)-H Cyanation of Sparteine and Lupanine in Undivided Batch and Continuous-Flow Cells.

Sparteine is widely used as a chiral ligand in asymmetric synthesis, but methods for providing efficient access to functionalized sparteine derivatives are still limited. Herein, we describe an electrochemical α-cyanation of sparteine-type bis-quinolizidine alkaloids. This method features commercially available setups for batch and single-pass continuous flow conditions, enabling easy gram scale synthesis of valuable racemic and enantiopure products. Moreover, insights into the selectivity of the reaction and overoxidation mechanisms are disclosed. This allows for the development of divergent oxidation pathways depending on the electrolysis conditions.

ScEMB: Learning context representation of genes based on large-scale single-cell transcriptomics.

The rapid advancement of single-cell transcriptomic technologies has led to the curation of millions of cellular profiles, providing unprecedented insights into cellular heterogeneity across various tissues and developmental stages. This growing wealth of data presents an opportunity to uncover complex gene-gene relationships, yet also poses significant computational challenges. We present scEMB, a transformer-based deep learning model developed to capture context-aware gene embeddings from large-scale single-cell transcriptomics data. Trained on over 30 million single-cell transcriptomes, scEMB utilizes an innovative binning strategy that integrates data across multiple platforms, effectively preserving both gene expression hierarchies and cell-type specificity. In downstream tasks such as batch integration, clustering, and cell type annotation, scEMB demonstrates superior performance compared to existing models like scGPT and Geneformer. Notably, scEMB excels in silico correlation analysis, accurately predicting gene perturbation effects in CRISPR-edited datasets and microglia state transition, identifying a few known Alzheimer's disease (AD) risks genes in top gene list. Additionally, scEMB offers robust fine-tuning capabilities for domain-specific applications, making it a versatile tool for tackling diverse biological problems such as therapeutic target discovery and disease modeling. scEMB represents a powerful tool for extracting biologically meaningful insights from complex gene expression data. Its ability to model in silico perturbation effects and conduct correlation analyses in the embedding space highlights its potential to accelerate discoveries in precision medicine and therapeutic development.

DEPICT-seq: Single-Cell Transcriptomic Analysis of Rare Cell Subsets Isolated via Nucleic Acid Cytometry.

The ability to dive deep into specific rare cell populations is critical for understanding tissue physiology and pathology across various biological domains. As single-cell RNA-seq flourishes, many newly discovered cell subtypes are defined by their transcriptomic markers. However, our ability to retrieve and analyze cells based on their nucleic acid markers remains underdeveloped. Here, we present Double Emulsion PCR-Initiated Cell sorting and Transcriptomic Sequencing (DEPICT-seq), a high-throughput droplet nucleic acid cytometry method that integrates batch cell fixation for cellular information preservation, double emulsion digital PCR-based cell sorting to target nucleic acid markers of interest, and in-depth full-length transcriptomic analyses at single-cell resolution. We utilize DEPICT-seq to isolate and characterize T cell receptor (TCR)-engineered T cells within a mixed population and also demonstrate a variation of the workflow by incorporating an RNase H-dependent PCR step to enrich full-length TCR sequences for paired single-cell TCR sequencing and transcriptomic profiling.

Decoupling Charge Carrier Electroreduction and Enzymatic CO2 Conversion to Formate Using a Dual-Cell Flow Reactor System.

With an efficient atom economy, low activation energy, and valuable applications for fuel cells and hydrogen storage, formic acid (FA) is a useful fuel product to convert CO2 and reduce emissions. Although metal catalysts are typically used for this conversion, unwanted side reactions remain a concern, particularly when products are attempted to be recovered long-term. In this study, an enzymatic catalyst is used to enable the selective conversion of CO2 to FA, as a formate ion. A dual-cell flow reactor system is used to first reduce a charge mediator electrochemically (reduction cell), which then activates a catalyst to selectively convert CO2 to formate (production cell). This approach minimizes enzyme degradation by avoiding direct contact with increased voltages and improves the quantity of formate produced. The system produced 25 mM of formate and reached over 50% Coulombic efficiency. The larger volume of this dual-cell system increases the quantity of formate produced beyond that of a batch cell. Additional design configurations are employed, including a pH control pump to maintain catalyst activity and a packed bed reactor to improve contact of the charge carrier with the catalyst. Both configurations retained higher production and efficiency long-term (∼168 h). The results highlight the challenges of developing a system where many parameters play a role in optimizing performance. Nevertheless, the ability of the system to produce formate from CO2 demonstrates the potential to improve upon this configuration for a variety of electrochemical CO2 conversion applications.

Heat capacity measurements by a Setaram μDSC3 evo microcalorimeter: estimation of deviation in the measurement, advanced data analysis by mathematical gnostics, and prediction by the artificial neural network

AbstractThe aim of the work is to study the variation in the isobaric heat capacity measurement due to changes in the amount of sample and the calibration standard using a Setaram $$\mu$$ μ DSC3 evo microcalorimeter batch cells to provide a guideline toward the selection of the sample amount to minimize heat capacity measurement error in $$\mu$$ μ DSC. Moreover, overall variation, variation due to the sample amount, and variation due to the calibration standard (reference) amount in heat capacity measurement were estimated for different amounts of the sample or/and the calibration standard material. In the present work, heat capacity measurements were taken for [C4mim][Tf2N] (1-butyl-3-methylimidazolium bis[(trifluoromethyl)sulfonyl]imide) ionic liquid as a sample material and 1-butanol as a calibration standard. A novel non-statistical approach, mathematical gnostics (MG), was used for data analysis of measured heat capacities data. Moreover, the artificial neural network (ANN) model was developed to predict the deviation in the heat capacity measurement with 99.83% accuracy and 0.9939 R2 score. The Python package PyCpep based on the trained ANN model was developed to predict the deviation in the heat capacity measurement.

Boosting seawater denitrification in an electrochemical flow cell

Nitrogen compounds in current seawater treatment processes typically are converted to nitrate, threatening seawater quality and marine ecology. Electrochemical denitrification is a promising technique, but its efficiency is severely limited by the presence of excess chloride ions. In this work, a flow-through cell went through an on-demand chlorine-mediated electrochemical-chemical tandem reaction process was designed for efficient seawater denitrification. Equipped with ultrathin cobalt-based nanosheets as the cathode catalyst and commercial IrO2-RuO2/Ti as the anode, the newly designed flow-through cell achieved nitrate removal efficiency that was about 50 times greater than the batch cell and nearly 100 % N2 selectivity. Moreover, nitrite and ammonia can also be removed with over 93 % efficiency in total nitrogen (TN) removal. Furthermore, the concentration of active chlorine in the effluent could be adjusted within two orders of magnitude, enabling on-demand release of active chlorine. Finally, this flow-through cell reduced the TN of actual mariculture tailwater (40.1 mg N L-1 nitrate) to only 5.7 mg N L-1, meeting the discharge standard for aquaculture tailwater of Fujian, China. This work demonstrates the paradigm of deep denitrification from ultra-concentrated chlorine ion wastewater using an on-demand active chlorine-mediated electrochemical-chemical tandem reaction process.

Electro-Oxidation of Cumene Using Diamond Electrodes in Batch and Flow

Selective oxidation of aromatic alkyl side chains is an important molecular transformation process to obtain various materials ranging from rubber and resin to fine chemicals. Generally, molecular oxygen has been utilized in straightforward oxidation of aromatic alkyls. However, since molecular oxygen is highly stable, activation of the molecular oxygen itself is necessary, which requires a specific catalyst and/or harsh condition such as high temperature and high pressure. Recent environmental and sustainable concerns lead to a growing demand for the development of greener oxidation processes.Recently, electro-organic synthesis, referring to an organic synthesis method combined with electrochemistry, has drawn attention as a sustainable synthetic method. Since electro-organic synthesis utilizes electricity itself as a reagent, reactions proceed well even under ambient conditions and reagent wastes are reduced to a minimum. In electro-organic synthesis, electrode materials are one of the most significant parameters because reactions occur at the anode and/or cathode. Boron-doped diamond (BDD) is a relatively new electrode material and shows a wide potential window, which can be applied to the transformation of compounds with high redox potentials. Therefore, BDD electrodes would enable a straightforward oxidation reaction of aromatic alkyls, which is difficult to achieve with other conventional electrode materials.Herein, we report the straightforward electro-oxidation of cumene, one of the most important and extensively investigated aromatic alkyls, by BDD electrodes. The role of electrode materials was investigated with electrochemical measurements. Furthermore, the cell design (i.e. batch vs. flow) was examined, and we realized that the product selectivity of cumene oxidation can be switched by simply changing the cell design.First, we carried out the electrolysis of cumene under the constant current condition in an undivided batch cell. The main product was acetophenone under the following optimum conditions: (solvent) MeCN, (supporting electrolyte) Et4NClO4, (current density) 2.1 mA/cm2, (amount of charge) 5 F referring to mole of cumene. It should be noted that almost no acetophenone was obtained when the combination of anode and cathode was graphite/graphite or Ni/Ni. Cyclic voltammetry using BDD as a working electrode showed a clear oxidation peak of cumene at around 2.40 V (vs. Ag/Ag+). On the other hand, no clear oxidation peak was observed when using graphite or Ni as a working electrode. This is because potential windows of graphite and Ni are too narrow to oxidize cumene directly. Overall, the electrochemical measurements indicate the BDD’s wide potential window enables direct oxidation of cumene to produce a key reaction intermediate to afford acetophenone. A series of electrolysis experiments confirmed that the reaction intermediate is the cumyl cation and the oxygen source is the superoxide generated by reduction of dissolved oxygen on the cathode. A proposed mechanism is as follows. Anodic oxidation of cumene on the BDD electrode affords a cumyl cation. On the other hand, cathodic reduction of dissolved oxygen produces the superoxide and even the hydroperoxide anion. Addition of the hydroperoxide anion to the cumyl cation yields cumene hydroperoxide, which is further converted into acetophenone.Second, we carried out the electrolysis of cumene under the constant current condition in an undivided flow cell. The main product was a-cumyl alcohol under the following optimum conditions: (solvent) MeCN, (supporting electrolyte) Et4NClO4, (current density) 0.25 mA/cm2, (amount of charge) 1 F referring to mole of cumene, (flow rate) 0.375 mL/min. A series of flow electrolysis experiments indicated that cumene is converted into a-cumyl alcohol via cumene hydroperoxide, and then to acetophenone, which is supported by cyclic voltammetry measurements: the potential at which the current value increases was lower in the order of cumene hydroperoxide > a-cumyl alcohol > cumene > acetophenone. A proposed mechanism is as follows. A hydroperoxide anion generated at the cathode added to a cumyl cation generated at the anode to form cumene hydroperoxide. The O-O bond in cumene hydroperoxidewas cleaved anodically to form alkoxy radical, which underwent a hydrogen atom transfer (HAT) between cumene to yield a-cumyl alcohol and cumyl radical. Furthermore, a-cumyl alcoholwas oxidized into the alkoxy radical, followed by b-scission to yield acetophenone. Therefore, it is suggested that the selective conversion of cumene into a-cumyl alcohol in flow electrolysis was due to suppressing overoxidation of a-cumyl alcoholinto acetophenone. Figure 1

(Invited) Scaling CO2 to MeOH Conversion in an Electrolyzer

Methanol is a desirable, value-added product from CO2 electroreduction, but is difficult to obtain in high Faradaic efficiencies. Promising results have emerged from a catalyst system comprised of cobalt pthalocyanine adsorbed onto carbon nanotubes (CoPc/CNT) in batch cell reactors, reaching Faradaic efficiencies of 40-50%.(1) In this talk, we will present our work on scaling this reaction in a 25 cm2 flow reactor with a gas diffusion electrode. We discuss the differences in the two geometries (electrolyzer vs. batch reactor) and the effect of different process conditions, such as pH, electrolyte identity and CO2 partial pressure on product selectivities. Y. Wu, Z. Jiang, X. Lu, Y. Liang, H. Wang, Nature. 575, 639–642 (2019).

Electrooxidation of Poly(methyl-methacrylate) and Poly(ethylene glycol): Hydrogen Production from Plastic Wastes Electrolysis

Introduction. Green H2 production by water electrolysis at low temperature has some bottlenecks mostly dealing with the anodic reaction, the oxygen evolution reaction (OER), given its thermodynamic and kinetic limitations [1]. One strategy to reduce the energy requirements for this H2 production route is the replacement of the conventional OER (with a thermodynamic potential of 1.23 V vs. Reversible Hydrogen Electrode (RHE)) by a less-energy intensive reaction like the electrooxidation of organic molecules (in some cases presenting a thermodynamic potential as low as c.a. 0 V vs RHE). This approach also addresses the possibility of converting organic wastes into value-added chemicals avoiding the CO2 emissions derived from conventional treatments (e.g., incineration) [2, 3]. Our research group is focused on the electrochemical valorization of several organic wastes like polyethylene glycol (PEG) or poly(methyl-methacrylate) (PMMA). Both are commonly found in urban and industrial water effluents and concern has been raised about the true fate of these polymers in the environment. Herein, the possibility of generating H2 and value-added organic molecules from these plastic wastes has been explored. Experimental. A binary solvent strategy was followed to dissolve the PMMA polymer, by using 1 M H2SO4 in 80% v/v 2-propanol/water electrolyte. Then the electrooxidation of PMMA was studied in a 2-electrode batch cell at 70ºC with different PMMA concentrations (0.1-2 wt.%). On the other hand, the electrooxidation of ethylene glycol (EG) and PEG with various molecular weights (from 200 to 4000 g mol-1), which are soluble in water, was carried out in aqueous solutions (PEG concentrations from 1 to 20 g/L) between 30 and 80 °C by using a Polymer Electrolyte Membrane (PEM) reactor. Pt/C electrodes were used in all cases. A commercial Pt cathode was employed and the anode material was prepared by the spray-deposition of a Pt(20 wt.%)/C catalyst powder onto a carbon paper gas-diffusion layer. The liquid products at the anodic compartment were analysed by size-exclusion chromatography (SEC) and nuclear magnetic resonance (NMR) spectroscopy while the production of hydrogen in the cathodic compartment was online monitored with a mass spectroscopy. Results and discussion. In the case of PMMA electrooxidation, despite the dissolution challenge, polymer macromolecules showed to partially block the accessibility of the Pt active sites on the electrode and strongly degraded its electrochemical performance [4]. In the case of PEG, it was much more easily electrooxidised, at low cell voltages, from around 0.4 V at 80°C (Figure 1a), near to values recorded with EG, suggesting a first step linked with the electrooxidation of the terminal groups of the macromolecules chain. Current densities larger than 100 mA.cm-2 at 0.8 V and 80°C were achieved for low molecular mass molecules (200 and 400 g/mol). A decrease of the PEG molecular weight in the anodic solution was demonstrated by SEC, confirming that the electro-oxidation process likely follows two main mechanisms, involving the consumption of terminal –OH or intermediate -C-O-C- bonds cleavage (Figure 1b), depending on the polymer molecular weight. This study demonstrates the proof-of-concept of the low-temperature electro-oxidation of polymers containing C-O bonds in their main chain. Conclusions. With a further macroporous electrode enegineering and catalyst development, the electrolysis of plastic wastes stands as a promising technology not only for the production of hydrogen from macromolecules by electrochemical means, but also as a potential pathway to depolymarize plastic wastes, thus increasing their degradability. References. [1]. Y. Cheng, S.P. Jiang, Advances in electrocatalysts for oxygen evolution reaction of water electrolysis-from metal oxides to carbon nanotubes, Prog. Nat. Sci.: Mater. 25 (2015) 545-553.[2]. M. Simões, S. Baranton, C. Coutanceau, Electrochemical valorisation of glycerol, ChemSusChem. 5 (2012) 2106–2124[3]. Y.X. Chen, A. Lavacchi, H.A. Miller, M. Bevilacqua, J. Filippi, M. Innocenti, et al. Nanotechnology makes biomass electrolysis more energy efficient than water electrolysis. Nat. Commun. 5 (2014) 1-6.[4]. N. Grimaldos-Osorio, F. Sordello, M. Passananti, J. González-Cobos, A. Bonhommé, P. Vernoux, A. Caravaca. From plastic-waste to H2: A first approach to the electrochemical reforming of dissolved Poly(methyl methacrylate) particles. Int. J. Hydrogen Energy. 48 (2023) 11899-11913. Acknowledgments The authors gratefully acknowledge the French institution “Ecole Urbaine de Lyon” (EUL - Institut de Convergences) for funding a PhD grant of Dr. N. Grimaldos-Osorio. Figure 1

Ala-Cys-Cys-Ala dipeptide dimer alleviates problematic cysteine and cystine levels in media formulations and enhances CHO cell growth and metabolism

Cysteine and cystine are essential amino acids present in mammalian cell cultures. While contributing to biomass synthesis, recombinant protein production, and antioxidant defense mechanisms, cysteine poses a major challenge in media formulations owing to its poor stability and oxidation to cystine, a cysteine dimer. Due to its poor solubility, cystine can cause precipitation of feed media, formation of undesired products, and consequently, reduce cysteine bioavailability. In this study, a highly soluble cysteine containing dipeptide dimer, Ala-Cys-Cys-Ala (ACCA), was evaluated as a suitable alternative to cysteine and cystine in CHO cell cultures. Replacing cysteine and cystine in basal medium with ACCA did not sustain cell growth. However, addition of ACCA at 4 mM and 8 mM to basal medium containing cysteine and cystine boosted cell growth up to 15% and 27% in CHO-GS and CHO–K1 batch cell cultures respectively and led to a proportionate increase in IgG titer. 13C-Metabolic flux analysis revealed that supplementation of ACCA reduced glycolytic fluxes by 20% leading to more efficient glucose metabolism in CHO–K1 cells. In fed-batch cultures, ACCA was able to replace cysteine and cystine in feed medium. Furthermore, supplementation of ACCA at high concentrations in basal medium eliminated the need for any cysteine equivalents in feed medium and increased cell densities and viabilities in fed-batch cultures without any significant impact on IgG charge variants. Taken together, this study demonstrates the potential of ACCA to improve CHO cell growth, productivity, and metabolism while also facilitating the formulation of cysteine- and cystine-free feed media. Such alternatives to cysteine and cystine will pave the way for enhanced biomanufacturing by increasing cell densities in culture and extending the storage of highly concentrated feed media as part of achieving intensified bioproduction processes.

Automated single-cell omics end-to-end framework with data-driven batch inference.

To facilitate single-cell multi-omics analysis and improve reproducibility, we present SPEEDI (Single-cell Pipeline for End to End Data Integration), a fully automated end-to-end framework for batch inference, data integration, and cell type labeling. SPEEDI introduces data-driven batch inference and transforms the often heterogeneous data matrices obtained from different samples into a uniformly annotated and integrated dataset. Without requiring user input, it automatically selects parameters and executes pre-processing, sample integration, and cell type mapping. It can also perform downstream analyses of differential signals between treatment conditions and gene functional modules. SPEEDI's data-driven batch inference method works with widely used integration and cell-typing tools. By developing data-driven batch inference, providing full end-to-end automation, and eliminating parameter selection, SPEEDI improves reproducibility and lowers the barrier to obtaining biological insight from these valuable single-cell datasets. The SPEEDI interactive web application can be accessed at https://speedi.princeton.edu/.

Theoretical performance optimization of enzymatic electrochemical CO2 reduction to formate: Voltage, concentration, temperature, pressure, and flow rate

Formic acid (FA) is a notable fuel product due to its atom economy, low activation energy, and applications in flow cells and hydrogen storage. While metal catalysts are typically used, selectivity remains a challenge. Here, an enzymatic catalyst is employed to selectively convert CO2 to FA as formate. This study documents the development of a computational model to examine the conversion of CO2 to formate under a wide range of conditions. The model examines the electrochemical reduction of a charge carrier, ethyl viologen (EV), and its subsequent use in an enzymatic catalyst to convert CO2 and protons in solution to formate. The model was first developed for a small-scale batch reactor, then later expanded to a dual-cell flow system, where the reduction of EV and production of formate are kept in separated cells, and flow rate is introduced as an additional variable parameter. While no studies have directly used all parameters addressed in the computations presented here, many of the conditions selected align with what has previously been used in experiments, and similar production rates and efficiencies are obtained. The most challenging parameters to study were charge carrier concentration and applied voltage, which showed optimal ranges in the cases studied for the batch and flow cell. While the study gives guidance toward which conditions would be favored experimentally to increase production rate and efficiency experimental studies should nonetheless be run at suggested optimal conditions to better adapt parameters made in both models.

Synthesis and characterization of an innovative sodium alginate/polyvinyl alcohol bioartificial hydrogel for forward-osmosis desalination.

Recently, hydrogels have been widely applied as draw agents in forward osmosis (FO) desalination. This work aims to synthesize bioartificial hydrogel from a blend of sodium alginate (SA) and polyvinyl alcohol (PVA) using epichlorohydrin (ECH) as a crosslinker. Then this prepared hydrogel was applied as a draw agent with cellulose triacetate membrane in a batch (FO) cell. The effects of the PVA content in the polymer blend and the crosslinker dose on the hydrogel's swelling capacity were investigated to optimize the hydrogel's composition. Furthermore, the water flux and the reverse solute flux of the optimum SA/PVA hydrogel were evaluated in a batch (FO) unit under the effect of the hydrogel's particle size, feed solution (FS) temperature, FS concentration, and membrane orientation. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD) and compression strength tests were used to characterize the prepared hydrogel. Results revealed that the equilibrium swelling ratio (%) of 5228 was achieved with a hydrogel that had 25% PVA and a crosslinking ratio of 0.8. FO experiments revealed that the maximum water flux of 0.845 LMH achieved, when distilled water was used as FS, average hydrogel's particle size was 60µm, and the FS temperature was 40°C.

Unlocking the Potential of Oxidative Asymmetric Catalysis with Continuous Flow Electrochemistry.

In the field of catalytic asymmetric synthesis, the less-treated path lies in oxidative catalytic asymmetric transformations. The hurdles of pinpointing the appropriate chemical oxidants and addressing their compatibility issues with catalysts and functionalities present significant challenges. Organic electrochemistry, employing traceless electrons for redox reactions, is underscored as a promising solution. However, the commonly used electrolysis in batch cells introduces its own set of challenges, hindering the advancement of electrochemical asymmetric catalysis. Here we introduce a microfluidic electrochemistry platform with single-pass continuous flow reactors that exhibits a wide-ranging applicability to various oxidative asymmetric catalytic transformations. This is exemplified through the sulfenylation of 1,3-dicarbonyls, dehydrogenative C-C coupling, and dehydrogenative alkene annulation processes. The unique properties of microfluidic electrochemical reactors not only eliminate the need for chemical oxidants but also enhance reaction efficiency and reduce the use of additives and electrolytes. These salient features of microfluidic electrochemistry expedite the discovery and development of oxidative asymmetric transformations. In addition, the continuous production facilitated by parallel single-pass reactors ensures straightforward reaction upscaling, removing the necessity for reoptimization across various scales, as evidenced by direct translation from milligram screening to hectogram asymmetric synthesis.

Robust platform for inline Raman monitoring and control of perfusion cell culture.

Perfusion cell culture has been gaining increasing popularity for biologics manufacturing due to benefits such as smaller footprint, increased productivity, consistent product quality and manufacturing flexibility, cost savings, and so forth. Process Analytics Technologies tools are highly desirable for effective monitoring and control of long-running perfusion processes. Raman has been widely investigated for monitoring and control of traditional fed batch cell culture process. However, implementation of Raman for perfusion cell culture has been very limited mainly due to challenges with high-cell density and long running times during perfusion which cause extremely high fluorescence interference to Raman spectra and consequently it is exceedingly difficult to develop robust chemometrics models. In this work, a platform based on Raman measurement of permeate has been proposed for effective analysis of perfusion process. It has been demonstrated that this platform can effectively circumvent the fluorescence interference issue while providing rich and timely information about perfusion dynamics to enable efficient process monitoring and robust bioreactor feed control. With the highly consistent spectral data from cell-free sample matrix, development of chemometrics models can be greatly facilitated. Based on this platform, Raman models have been developed for good measurement of several analytes including glucose, lactate, glutamine, glutamate, and permeate titer. Performance of Raman models developed this way has been systematically evaluated and the models have shown good robustness against changes in perfusion scale and variations in permeate flowrate; thus models developed from small lab scale can be directly transferred for implementation in much larger scale of perfusion. With demonstrated robustness, this platform provides a reliable approach for automated glucose feed control in perfusion bioreactors. Glucose model developed from small lab scale has been successfully implemented for automated continuous glucose feed control of perfusion cell culture at much larger scale.

Timely delivery of bone marrow mesenchymal stem cells based on the inflammatory pattern of bone injury environment to promote the repair of calvarial bone defects in rats: An optimized strategy for bone tissue engineering

Stem cell-based therapy plays a significant role in the repair of bone defects. However, traditional stem cell transplantation strategies in bone tissue engineering are characterized by low survival rates and unstable treatment outcomes. In this study, we propose a timely delivery strategy for inflammatory changes in the setting of bone injury to improve the survival rate of transplanted cells and bone repair. The results of cell tracing in vivo showed that this strategy could effectively improve the survival rate of low-dose exogenous transplanted cells in bone defect areas, and CD31 immunofluorescence and histological sections suggested that this strategy effectively promoted vascularization and new bone formation in the calvarial defect area. Subsequently, we analyzed the mechanism of action of the “Two-step” strategy from the perspective of inflammatory microenvironment regulation, and the results suggested that the first batch transplanted stem cells caused localized and transient increases in tissue apoptosis levels and inflammatory factors, and recruited macrophage chemotaxis, and the second batch of cells may promote pro-inflammatory - anti-inflammatory transformation of the tissue. Finally, mRNA sequencing results suggest that the first batch cells in the “Two-step” strategy are important initiators in bone repair, which not only actively regulate the immune microenvironment at the bone defect, but also guide richer cellular activity and more positive biochemical responses. Therefore, the “Two-step” strategy leads to efficient inflammatory environment regulation and superior bone repair effects, which may provide an alternative option for the treatment of bone defects in the future.

Enhancing electrochemical reactions in organic synthesis: the impact of flow chemistry.

Utilizing electrons directly offers significant potential for advancing organic synthesis by facilitating novel reactivity and enhancing selectivity under mild conditions. As a result, an increasing number of organic chemists are exploring electrosynthesis. However, the efficacy of electrochemical transformations depends critically on the design of the electrochemical cell. Batch cells often suffer from limitations such as large inter-electrode distances and poor mass transfer, making flow cells a promising alternative. Implementing flow cells, however, requires a foundational understanding of microreactor technology. In this review, we briefly outline the applications of flow electrosynthesis before providing a comprehensive examination of existing flow reactor technologies. Our goal is to equip organic chemists with the insights needed to tailor their electrochemical flow cells to meet specific reactivity requirements effectively. We also highlight the application of reactor designs in scaling up electrochemical processes and integrating high-throughput experimentation and automation. These advancements not only enhance the potential of flow electrosynthesis for the synthetic community but also hold promise for both academia and industry.

Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial.

The emergence and development of single-cell RNA sequencing (scRNA-seq) techniques enable researchers to perform large-scale analysis of the transcriptomic profiling at cell-specific resolution. Unsupervised clustering of scRNA-seq data is central for most studies, which is essential to identify novel cell types and their gene expression logics. Although an increasing number of algorithms and tools are available for scRNA-seq analysis, a practical guide for users to navigate the landscape remains underrepresented. This chapter presents an overview of the scRNA-seq data analysis pipeline, quality control, batch effect correction, data standardization, cell clustering and visualization, cluster correlation analysis, and marker gene identification. Taking the two broadly used analysis packages, i.e., Scanpy and MetaCell, as examples, we provide a hands-on guideline and comparison regarding the best practices for the above essential analysis steps and data visualization. Additionally, we compare both packages and algorithms using a scRNA-seq dataset of the ctenophore Mnemiopsis leidyi, which is representative of one of the earliest animal lineages, critical to understanding the origin and evolution of animal novelties. This pipeline can also be helpful for analyses of other taxa, especially prebilaterian animals, where these tools are under development (e.g., placozoan and Porifera).

The impact of batch flotation tests on the industrial plant prediction

To predict the metallurgical results of industrial plants, laboratory batch flotation tests are carried out, relating both operations through scale-up factors. However, robust scale-up procedures and well-defined laboratory protocols are necessary to reach reliable results at industrial scale. In this paper, results from different flotation batch tests are presented, analysing the effect of batch protocol in terms of operating conditions, operator, ore type, water quality, and others on the metallurgical response. Additionally, the ability of batch responses to describe industrial operations and determine scale-up factors was analysed. Then, a sensitivity analysis was carried out, considering the effect of batch and industrial conditions on the estimation of scale-up factors. Results showed that the batch response significantly changes, depending on the operating conditions, flotation operator, and batch flotation machine, even for the same cell design. Additionally, it was observed that the batch recovery significantly changes when modifying ore type and water quality, which can cause changes in batch and/or industrial operation, affecting the scale-up factors. In addition, results showed that the scale-up factors varied significantly by changing operating conditions in a batch cell. This also occurs in plants when metallurgical performance changes, for example, due to a modification in launder design and/or operating condition, to increase recovery, or due to control limitations that prevent efficient metallurgical performance.