Batch Setup Research Articles

Serial batching to minimize the weighted number of tardy jobs

AbstractThe $$1\vert \text {s-batch}(\infty ),r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j scheduling problem takes as input a batch setup time $$\Delta $$ Δ and a set of n jobs, each having a processing time, a release date, a weight, and a due date; the task is to find a sequence of batches that minimizes the weighted number of tardy jobs. This problem was introduced by Hochbaum and Landy (Oper Res Lett 16(2):79–86, 1994); as a wide generalization of Knapsack, it is $$\textsf{NP}$$ NP -hard. In this work, we provide a multivariate complexity analysis of the $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j problem with respect to several natural parameters. That is, we establish a classification into fixed-parameter tractable and $$\textsf{W}[1]$$ W [ 1 ] -hard problems, for parameter combinations of (i) $$\#p$$ # p = number of distinct processing times, (ii) $$\#w$$ # w = number of distinct weights, (iii) $$\#d$$ # d = number of distinct due dates, (iv) $$\#r$$ # r = number of distinct release dates. Thereby, we significantly extend the work of Hermelin et al. (Ann Oper Res 298:271–287, 2018) who analyzed the parameterized complexity of the non-batch variant of this problem without release dates. As one of our key results, we prove that $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j is $$\textsf{W}[1]$$ W [ 1 ] -hard parameterized by the number of distinct processing times and distinct due dates. To the best of our knowledge, these are the first parameterized intractability results for scheduling problems with few distinct processing times. Further, we show that $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j is fixed-parameter tractable parameterized by $$\#d + \#p + \#r$$ # d + # p + # r , and parameterized by $$\#d + \#w$$ # d + # w if there is just a single release date. Both results hold even if the number of jobs per batch is limited by some integer b.

Advanced Processes in Water Treatment: Synergistic Effects of Hydrodynamic Cavitation and Cold Plasma on Rhodamine B Dye Degradation

The increasing pollution of water bodies, due to the constant release of highly toxic and non-biodegradable organic pollutants, requires innovative solutions for environmental remediation and wastewater treatment. In this study, the effectiveness of different Advanced Oxidation Processes (AOPs) for the purification of water contaminated with Rhodamine B (RhB) dye at a concentration of 5 mg/L were investigated and compared. Using the classical ozonation strategy as a benchmark treatment, the research showed over 99% degradation of RhB within 4 min in a laboratory-scale batch setup with a capacity of 0.2 L. In contrast, a “chemical-free” process exploiting ultrasound (US) technology achieved a 72% degradation rate within 60 min. Further experiments were conducted using a pilot-scale rotor-stator hydrodynamic cavitation (HC) reactor on a 15 L solution leading to 33% of RhB removal in the presence of hydrogen peroxide (H2O2) at 75 mg/L. However, the use of an innovative cavitational reactor, which hybridizes HC with cold plasma, showed remarkable efficiency and achieved 97% degradation of RhB in just 5 min when treating a 5 L solution at an inlet pressure of 20 bar in a loop configuration. In addition, a degradation rate of 58% was observed in a flow-through configuration, emphasising the robustness and scalability of the HC/electrical discharge (ED) plasma technology. These results underline the potential of hybrid HC/ED plasma technology as an intensified and scalable process for the purification of water, as it offers a catalyst- and oxidant-free protocol.

Hydrogel immobilized microalgae-alginate beads to model the fermentation of phenol-containing wastewater into biohydrogen molecules

The potential of hydrogel immobilized microalgae-alginate beads to concurrently remove phenol and produce biohydrogen molecules from phenol-containing wastewaters with different phenol concentrations of 0.2 g/L to 1.2 g/L via dark fermentation had been successfully modelled and proven. Highest biohydrogen production and phenol removal were achieved at phenol concentration of 1.2 g/L. The lag phase of biohydrogen production increased with increasing of phenol concentration as the microalgal cells entailed a longer time to acclimatize in inhibitory medium of high phenol strength. The extended period for fermentation could also generate more biohydrogen molecules as compared with batch setup that was easily inhibited due to inadequate acclimatization allocation. Then, a new kinetic model was rederived from the modified Gompertz model and Andrew’s inhibition model to describe the relationship between phenol concentration impacting the microalgal growth and subsequent biohydrogen production. Low concentration of phenol could serve as an alternative source of carbon to promote the microalgal growth and development; but high phenol concentration could hinder the growth of microalgae, afflicting the fermentative biohydrogen production. The rederived kinetic model was able to fit the experimental data for all phenol concentrations with coefficients of determination of greater than 0.95. This study had ultimately evidenced that the immobilized microalgae were able to ferment phenol and produce biohydrogen molecules simultaneously from phenol-containing wastewater.

Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning

Anomaly detection (AD) task corresponds to identifying the true anomalies among a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies. The ranked list of anomalies is then analyzed by a human to discover the true anomalies. Ensemble of tree-based anomaly detectors trained in an unsupervised manner and scoring based on uniform weights for ensembles are shown to work well in practice. However, the manual process of analysis can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. One important way to configure the detector is by providing true labels (nominal or anomaly) for a few instances. Recent work on active anomaly discovery has shown that greedily querying the top-scoring instance and tuning the weights of ensembles based on label feedback allows us to quickly discover true anomalies. This paper makes four main contributions to improve the state-of-the-art in anomaly discovery using tree-based ensembles. First, we provide an important insight that explains the practical successes of unsupervised tree-based ensembles and active learning based on greedy query selection strategy. We also show empirical results on real-world data to support our insights and theoretical analysis to support active learning. Second, we develop a novel batch active learning algorithm to improve the diversity of discovered anomalies based on a formalism called compact description to describe the discovered anomalies. Third, we develop a novel active learning algorithm to handle streaming data setting. We present a data drift detection algorithm that not only detects the drift robustly, but also allows us to take corrective actions to adapt the anomaly detector in a principled manner. Fourth, we present extensive experiments to evaluate our insights and our tree-based active anomaly discovery algorithms in both batch and streaming data settings. Our results show that active learning allows us to discover significantly more anomalies than state-of-the-art unsupervised baselines, our batch active learning algorithm discovers diverse anomalies, and our algorithms under the streaming-data setup are competitive with the batch setup.

Two-stage thermal pyrolysis of plastic solid waste: Set-up and operative conditions investigation for gaseous fuel production

In response to the escalating global challenge of mounting plastic waste and the imperative to adopt more sustainable practices for resource utilization, our study focuses on the utilization of plastic solid waste (PSW) through a two-stage thermal pyrolysis process. This aims to demonstrate its potential as a high-performance alternative to existing two-stage catalytic pyrolysis methods. The experimentation involved processing real scrap PSW material in a lab-scale batch set-up, emphasizing optimizing residence time in the cracking reactor to maximize gas yield and its lower heating value (LHV). The study underscores the advantages of the employed two-stage thermal pyrolysis apparatus through a comparative analysis with established set-up dedicated to maximizing gas yield. Once the operative conditions were explored, resulting pyrolysis products underwent detailed characterization to assess their suitability as a sustainable fuel source. The study also presents a practical application of the produced gaseous fuel, envisioning its combustion in an internal combustion engine (ICE), known for its flexibility regarding fuel properties. This application is demonstrated through a simulation conducted in Unisim Design©. The successful processing of real PSW material in the two-stage lab-scale experimental set-up showcased optimal gas yield achievements (>65 % w/w) with an LHV (∼41 MJ/kg), comparable to that of natural gas. This emphasizes the potential of these sustainable alternatives to replace fossil fuels, especially in the context of ICE applications. The integration of the pyrolysis plant with an ICE demonstrated promising prospects for generating electricity in the transportation sector and facilitating thermal power for heat integration in pyrolysis reactors.

Production of large quantity of plasma activated water using multiple plasma device setup

This study focuses on large-scale production of plasma-activated water (PAW) using batch processes with circulating and non-circulating modes. A multi-plasma device setup, powered by a self-developed high-voltage high-frequency supply. This plasma is electrically characterized and the produced radicals/species are identified using emission spectroscopy, with resulting effluent gases containing polluting species. The PAW setup utilizes these gases to create substantial quantities of PAW, decreasing environmental pollutants. The non-circulating mode produces highly reactive PAW (2L/batch) while the circulating mode yields larger volumes (20L/batch) with lower reactivity. Applications range from microbial inactivation to agriculture. Both batch setups are scalable for industrial PAW production.

Conversion of char from pyrolysis of plastic wastes into alternative activated carbons for heavy metal removal

The valorization of post-consumer mixed plastics in pyrolysis processes represents an abundant reservoir of carbon that can be effectively converted into useful chars. This process not only holds appeal in terms of improving plastic waste concerns but also contributes to the reduction of greenhouse gas emissions, thus aligning with the principles of a circular economy paradigm. In this study, the char produced from the pyrolysis of post-consumer mixed plastic waste has been activated with Na2CO3, KOH, NaOH, and K2CO3 to improve the textural, structural, and composition characteristics, leading to improved adsorption capability. These characteristics were studied by N2 adsorption-desorption isotherms, scanning electron microscopy, elemental and immediate analysis, and X-ray photoelectron spectroscopy. The developed surface area (SBET) was 573, 939, 704 and 592 m2 g−1 for Na2CO3, KOH, NaOH and K2CO3 activated carbons, respectively. These activated chars (ACs) were tested for the adsorption of heavy metals in both synthetic waters containing Pb, Cd, and Cu and industrial wastewater collected at an agrochemical production plant. Na2CO3-AC was the best performing material. The metal uptake in synthetic waters using a batch set-up was 40, 13 and 12 mg g−1 for Pb, Cd and Cu. Experiments in a column set-up using Na2CO3-AC resulted in a saturation time of 290, 16, and 80 min for Pb, Cd, and Cu synthetic waters, respectively, and metal uptakes of 26.8, 4.1, and 7.9 mg g−1, respectively.The agrochemical effluents, containing mainly Cr, Cu, Mn, and Zn were tested in a plug-flow column. The metal uptake notably decreased compared to synthetic water due to a competition effect for active sites.

OptMSP: A toolbox for designing optimal multi-stage (bio)processes

One central goal of bioprocess engineering is to maximize the production of specific chemicals using microbial cell factories. Many bioprocesses are one-stage (batch) processes (OSPs), in which growth and product synthesis are coupled. However, OSPs often exhibit low volumetric productivities due to the competition for substrate for biomass and product synthesis implying trade-offs between biomass and product yields. Two-stage or, more generally, multi-stage processes (MSPs) offer the potential to tackle this trade-off for improved efficiency of bioprocesses, for example, by separating growth and production. MSPs have recently gained much attention, also because of a rapidly growing toolbox for the dynamic control of metabolic fluxes. Despite these promising advancements, computational tools specifically tailored for the optimal design of MSPs in the field of biotechnology are still lacking.Here, we present OptMSP, a new Python-based toolbox for identifying optimal MSPs maximizing a user-defined process metrics (such as volumetric productivity, yield, and titer or combinations thereof) under given constraints. In contrast to other methods, our framework starts with a set of well-defined modules representing relevant stages or sub-processes. Experimentally determined parameters (such as growth rates, substrate uptake and product formation rates) are used to build suitable ODE models describing the dynamic behavior of each module. OptMSP finds then the optimal combination of those modules, which, together with the optimal switching time points, maximize a given objective function. We demonstrate the applicability and relevance of the approach with three different case studies, including the example of lactate production by E. coli in a batch setup, where an aerobic growth phase can be combined with anaerobic production phases with or without growth and with or without enhanced ATP turnover.

CYCLOALKYLATION OF PARA-CHLOROPHENOL WITH 1-METHYL-CYCLOALKENES IN THE PRESENCE OF A ZEOLITE-CONTAINING CATALYST

The results of alkylation reactions of para-chlorophenol with 1-methylcyclopentene and 1-methylcyclohexene in the presence of zeolite-containing catalyst Seokar-2 (TU38-101483-77) are presented in the paper. Para-chlorophenol (PCP) interacted with 1-methylcycloalkenes on a laboratory batch setup. The reaction products were separated from the catalyst by hot filtration (40-50 °C) and undergone to distillation. Methylcyclene was distilled off at atmospheric pressure, then unreacted para-chlorophenol and the target reaction product were isolated under vacuum (10 mm Hg). Optimal conditions ensuring maximum yield of 2-cycloalkyl-4-chlorophenols were determined by studying effect of temperature, reaction time, molar ratio of PCP to 1-methylcycloalkenes, and amount of catalyst on the yield and selectivity. The reaction temperature was varied from 80 to 140 °C, duration of the experiments was from 2 to 8 h, molar ratio of PCP to 1-methylcycloalkenes was from 2:1 to 1:2 mol/mol, amount of catalyst was from 5 to 15% mas. It resulted in determining of the optimal conditions for alkylation reaction of para-chlorophenol with 1-methylcyclopentene and 1-methylcyclohexene in the presence of the zeolite-containing catalyst Seokar-2: reaction temperature 110-120 °C, duration 4-6 h, molar ratio of PCP to 1-methylcycloalkenes 1:1, catalyst amount 12-15% (on the taken PCP). These conditions allow having 68.2-73.6% yield of theory for 2-cycloalkyl-4-chlorophenols for the taken PCP, and 89.3-91.8% of selectivity for the target product. The physicochemical properties, elemental composition and structure were determined by 1H NMR and IR spectroscopy after separation of the target products from alkylate by distillation.

Zinc removal from metallurgical dusts with iron- and sulfur-oxidizing bacteria

The generation of currently partially recyclable metallurgical dusts during the steel production represents a challenge for the realization of a circular economy in this industrial sector. Due to a high iron concentration, the reutilization of blast furnace and cast house dusts in internal crude steel processes, is state of the art. However, heavy metal impurities such as zinc, act as an interference due to diverse negative effects. The aim of this study is to utilize Acidithiobacillus ferrooxidans, an iron- and sulfur-oxidizing bacteria, to solubilize zinc whilst retaining iron in the dust residue intended for reuse. To achieve this, the biooxidation of elemental sulfur is of crucial relevance. Two different strains of A. ferrooxidans (DSM 583 and CCM 4253) were cultivated and their leaching performances were compared to each other using a stirred tank reactor operated in a fed batch set-up, with a working volume of 3 L, at an operation temperature of 30 °C. The dust concentration was gradually increased until a maximum of 125 g/L, after which the reactors were left to run for three additional cycles to ensure replicability of results. For both strains, at a cast house dust concentration of 125 g/L paired with a leaching duration of two weeks, an average zinc removal of 56 % was achieved whereas solely an average of 4 % of iron was leached.

Combined CFD and Heat Treatment Simulation of High-Pressure Gas Quenching Process

Abstract To improve the process development for high pressure gas quenching a digital quenching simulation model combining the fields of computational fluid dynamics and heat treatment process simulation has been developed. It was found that the gas flow and hence the quenching properties depend on both local (geometry of parts, carrier and chamber) as well as global influencing factors (fan characteristics, system pressure and hydraulic resistances). Therefore, a computational fluid dynamics model that includes all these factors was realized. The approach includes a heat transfer analysis to determine the local heat exchange coefficients on a component level. By connecting the computational fluid dynamics model and heat treatment simulation the local quenching characteristics are used to compute the temperature history of the quenched part. Based on a thermo-metallurgical heat treatment simulation the computed local cooling curves and metallurgical phase compositions are used to accurately predict the part properties like microstructure and hardness. The applicability of the model has been confirmed by hardness measurements. Hardness results for different batch positions, batch setups or tray systems can now be computed enabling an efficient virtual development of the gas quenching process.

Evaluation of Long-Term Fermentation Performance with Engineered Saccharomyces cerevisiae Strains

The performance of a microbial fermentation on an industrial scale is subjected to the robustness of the strain. Such strains are genetically engineered to optimize the production of desired compounds in minimal time, but they often fail to maintain high productivity levels for many generations, hindering their effective application in industrial conditions. This study focused on assessing the impact of genomic instability in yeasts that were engineered to produce a fluorescent output by incorporating a reporter gene at one or more genomic locations. The fermentation performance of these strains was evaluated over 100 generations in a sequential batch set-up. In order to bridge the gap between strain engineering and industrial implementation, we proposed the use of novel, host-specific parameters to standardize the strain robustness and evaluate potential improvements. It was observed that yeasts carrying multiple copies of the reporter gene exhibited a more pronounced decrease in output, and the genomic integration site significantly influenced the production. By leveraging these new, host-specific parameters, it becomes possible to anticipate strain behavior prior to incurring substantial costs associated with large-scale production. This approach enhances the economic viability of novel microbial fermentation processes and narrows the divide between laboratory findings and industrial applications.

Exploring HTL pathways in carbohydrate–protein mixture: a study on glucose–glycine interaction

The hydrothermal liquefaction (HTL) of biomass is a strategic process to convert wet and waste feedstocks into liquid biofuel. In this work, we investigated the hydrothermal liquefaction of glucose and glycine, alone and together, to mimic the composition of low-lipid content biomass. Experimental tests were performed in a batch setup in the temperature range of 200–350 °C. As the feeding composition and temperature changed, the distribution among the different phases (gas, solid, biocrude, and aqueous phase) and their compositions were evaluated through different analytical techniques (GC–MS, µ-GC, HPLC). Glucose–glycine showed strongly different interactions with reaction temperature: increased biocrude production at high temperature and increased solid production at low temperature, following a proportionally inverse trend. Biocrude, as well as all the other phases, was observed to be completely different according to the feedstock used. To study how their formation and mutual interactions were affected by the composition of the starting feedstock, consecutive reactions of the generated phases were innovatively carried out. The solid phase generated from glucose–glycine interaction at low temperatures was experimentally observed to be mostly converted into biocrude at high temperatures. Furthermore, no interaction phenomena between the different phases were observed with glucose–glycine, while with glucose alone the co-presence of the molecules in the different phases seemed to be the cause for the lowest biocrude yield at high temperatures. The results obtained in this work can provide new insights into the understanding of hydrothermal liquefaction of low-lipid biomass, pointing out synergetic phenomena among both the biomolecules and the resulting phases.

Parametric study for the treatment of tannery dye wastewater by electro-oxidation

Degradation of tannery effluents is a difficult operation because of their intricate chemical structures. Most of the conventional approaches are becoming ineffective because of the wide variation in the composition of tannery effluent. Dyes from the wastewater is also dangerous, as the wastewater has negative impact on the health of human being, plants and aquatic animals. For the first time once through continuous approach was employed for the removal of Acid yellow 110 tannery dye in electro-oxidation process on Mix Metal Oxide of ruthenium and iridium on titanium sheet electrode. The effects of pH, time (t) and current (i) on % color removal and energy consumed were investigated in a batch setup and found the optimum condition with the help of RSM design. The values of the responses Y1 and Y2 were found to be 93.08% and 1.07 kWh/m3 respectively at the optimum condition. Toxicity Bioassay analysis, GC-MS analysis and the kinetic study were performed at optimum condition. At the flow rates of 10–40 ml/min once through continuous experiments was conducted to found the feasibility of the process at pilot scale or industrial scale application.

Enabling batch and microfluidic non-thermal plasma chemistry: reactor design and testing.

Non-thermal plasma (NTP) is a promising state of matter for carrying out chemical reactions. NTP offers high densities of reactive species, without the need for a catalyst, while operating at atmospheric pressure and remaining at moderate temperature. Despite its potential, NTP cannot be used comprehensively in reactions until the complex interactions of NTP and liquids are better understood. To achieve this, NTP reactors that can overcome challenges with solvent evaporation, enable inline data collection, and achieve high selectivity, high yield, and high throughput are required. Here, we detail the construction of i) a microfluidic reactor for chemical reactions using NTP in organic solvents and ii) a corresponding batch setup for control studies and scale-up. The use of microfluidics enables controlled generation of NTP and subsequent mixing with reaction media without loss of solvent. The construction of a low-cost custom mount enables inline optical emission spectroscopy using a fibre optic probe at points along the fluidic pathway, which is used to probe species arising from NTP interacting with solvents. We demonstrate the decomposition of methylene blue in both reactors, developing an underpinning framework for applications in NTP chemical synthesis.

Novel Route to Produce Hydrocarbons from Woody Biomass Using Molten Salts.

The thermochemical decomposition of woody biomass has been widely identified as a promising route to produce renewable biofuels. More recently, the use of molten salts in combination with pyrolysis has gathered increased interest. The molten salts may act as a solvent, a heat transfer medium, and possibly also a catalyst. In this study, we report experimental studies on a process to convert woody biomass to a liquid hydrocarbon product with a very low oxygen content using molten salt pyrolysis (350–450 °C and atmospheric pressure) followed by subsequent catalytic conversions of the liquids obtained by pyrolysis. Pyrolysis of woody biomass in molten salt (ZnCl2/NaCl/KCl with a molar composition of 60:20:20) resulted in a liquid yield of 46 wt % at a temperature of 450 °C and a molten salt/biomass ratio of 10:1 (mass). The liquids are highly enriched in furfural (13 wt %) and acetic acid (14 wt %). To reduce complexity and experimental issues related to the production of sufficient amounts of pyrolysis oils for further catalytic upgrading, model studies were performed to convert both compounds to hydrocarbons using a three-step catalytic approach, viz., (i) ketonization of acetic acid to acetone, (ii) cross-aldol condensation between acetone and furfural to C8–C13 products, followed by (iii) a two-stage catalytic hydrotreatment of the latter to liquid hydrocarbons. Ketonization of acetic acid to acetone was studied in a continuous setup over a ceria–zirconia-based catalyst at 250 °C. The catalyst showed no signs of deactivation over a period of 230 h while also achieving high selectivity toward acetone. Furfural was shown to have a negative effect on the catalyst performance, and as such, a separation step is required after pyrolysis to obtain an acetic-acid-enriched fraction. The cross-aldol condensation reaction between acetone and furfural was studied in a batch using a commercial Mg/Al hydrotalcite as the catalyst. Furfural was quantitatively converted with over 90% molar selectivity toward condensed products with a carbon number between C8 and C13. The two-stage hydrotreatment of the condensed product consisted of a stabilization step using a Ni-based Picula catalyst and a further deep hydrotreatment over a NiMo catalyst, in both batch setups. The final product with a residual 1.5 wt % O is rich in (cyclo)alkanes and aromatic hydrocarbons. The overall carbon yield for the four-step approach, from pinewood biomass to middle distillates, is 21%, assuming that separation of furfural and acetic acid after the pyrolysis step can be performed without losses.

Efficient synthesis of high-silica SSZ-13 zeolite and its catalytic performance in MTO reaction

Highly efficient synthesis of zeolites is an important goal given the many catalytic applications of zeolites in industrial chemical processes. The conventional zeolite syntheses, i.e., hydrothermal synthesis in a batch setup are usually challenged by certain limiting factors deriving from the long synthesis period or the low yield. Herein, we report that a combined strategy of organic promoter and zeolite seeding can remarkably improve the synthetic efficiency of high silica SSZ-13 zeolite. The low-cost promoter diethylamine (DEA) and zeolite seeds could both remarkably accelerate the crystallization rate of SSZ-13 zeolite, and DEA has a much higher beneficial effect on the solid yield as compared to the zeolite seeds. Consequently, the crystallization time could be unprecedentedly shorted by a factor of 30, instead of multiple days normally required. Besides, this novel protocol also exhibits high atom efficiency (93% of yield). The resultant high-silica nano-sized SSZ-13 zeolite shows good (hydro)thermal stability along with excellent catalytic performance for the MTO reaction.

Parametric Study of Methyl Orange Removal Using Metal–Organic Frameworks Based on Factorial Experimental Design Analysis

Wastewater treatment plants (WWTPs) are one of the most energy-intensive industries. Every stage of wastewater treatment consumes energy, which is the primary contributor to WWTP costs. Adsorbents and process optimization are critical for energy savings. The removal of dyes from industrial wastewater by adsorption using commercially available adsorbents is inefficient. Metal–organic frameworks (MOFs) have outstanding properties that can improve separation performance over current commercial adsorbents, and thus, these materials represent a milestone in improving dye removal in water treatment methods. In this work, three types of metal–organic frameworks (Fe-BTC, Cu-BTC, and ZIF-8) have been investigated as prospective adsorbents for methyl orange removal from water in batch setups. The results showed that at 15 mg/L MO initial concentration and 100 mg dosage, Fe-BTC had the highest removal efficiency of 91%, followed by ZIF-8 (63%), and finally Cu-BTC (35%), which exhibited structural damage due to its instability in water. Fe-BTC maintained consistent adsorption capacity over a wide range of pH values. Furthermore, a 23 full factorial design analysis was implemented to evaluate the conditions for maximum MO-removal efficiency. The main effects, interaction effects, analysis of variance (ANOVA), and the Pareto chart were reported. The statistical analysis demonstrated that the MOF type was the most significant factor, followed by dosage and initial concentration. The analysis indicated that the type of MOF and dosage had a positive effect on the removal efficiency, while the initial concentration had a negative effect. The two-way and three-way interactions were also found to be significant.

Abstract 3908: A streamlined and automated approach to high-content cytometric immunophenotyping with CyTOF XT

Abstract High-parameter immune profiling is crucial in translational and clinical research to quantify changes in immune cell populations over time. CyTOF® mass cytometry is a high-plex, single-cell analysis platform that uses isotopically pure metal-labeled antibodies. The major advantage of CyTOF is its ability to resolve 40-plus markers in a single panel without compensation, making mass cytometry ideal for routine immunophenotyping. The autosampler module of CyTOF XT™ provides significant time savings by allowing automated sample acquisition. Tubes of pelleted stained samples are loaded into the Autosampler carousel, and the samples are resuspended with EQ™ Calibration Beads for acquisition. User input is only required during instrument startup, tuning, and batch setup. The added automation of CyTOF XT provides a streamlined workflow for suspension mass cytometry. Testing was performed to ensure that the data obtained on CyTOF XT was comparable to manual acquisition systems. The performance of CyTOF XT was tested in parallel with its predecessor, Helios™. Several workflows and applications for suspension mass cytometry including sample barcoding with the Cell-ID™ 20-Plex Pd Barcoding Kit, and surface, cytoplasmic, and nuclear staining and phosphostaining were evaluated on human PBMC. Manual gating analysis was performed to assess population frequencies and median intensities for each marker. Resolution index was calculated to assess how well positive and negative populations separated from each other. There was no significant difference between population frequencies analyzed between the two CyTOF systems. Moreover, samples acquired on CyTOF XT, on average, resulted in greater signal resolution between positive and negative populations compared to Helios. The Maxpar Direct Immune Profiling System was also compared on CyTOF XT and Helios using human whole blood and PBMC. The Maxpar® Direct™ Immune Profiling Assay™ and Maxpar Pathsetter™ software were developed as a sample-to-answer system for human immune profiling using CyTOF. The Maxpar Direct Immune Profiling Assay includes an optimized panel of 30 unique markers in a dry, single-tube format. Maxpar Pathsetter is an automated software used to report population statistics, stain assessments, and relevant data plots for the panel. The automated staining assessment in Maxpar Pathsetter was compared between files acquired on CyTOF XT and Helios. Comparable population frequencies were obtained between the two acquisition systems, and improved staining assessment was observed on CyTOF XT. Overall, these studies demonstrate that CyTOF XT generates better signal resolution as compared to Helios. The automated acquisition of CyTOF XT enables researchers to streamline immunophenotyping of human samples while accurately and reproducibly monitoring changes in immune cell subsets. For Research Use Only. Not for use in diagnostic procedures. Citation Format: Thiru Selvanantham, Stephen K.H. Li, Nick Zabinyakov, Alexandre Bouzekri, Raymond Jong, Michael Sullivan, Alexander Laboda, Daniel Majonis, Christina Loh. A streamlined and automated approach to high-content cytometric immunophenotyping with CyTOF XT [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3908.

Supercritical CO2 pretreatment of date fruit biomass for enhanced recovery of fruit sugars

This study explores the use of supercritical CO2 (Sc-CO2) as an innovative and green approach for pretreating date fruits to enhance the total sugar yield by water-based extraction. The pretreatment step was investigated based on Box-Behnken factorial design to evaluate the influence of critical variables on the total sugar yield. Sc-CO2 conditioning of the date pulp was investigated by varying the pressure (10–20 MPa), temperature (25–50 °C), and contact time (30–120 min) in a batch setup. Temperature and pressure variables were identified as the significant parameters for the Sc-CO2 treatment. Optimization analysis showed a maximum total sugar yield of 74.1 g/100 g for Sc-CO2pretreated date pulp at 10 MPa and 46 °C for 59 min. Besides, the pretreatment mechanism involved major effects such as swelling, disruption, and explosion that favored the enhanced recovery of total sugars from the date fruit matrix during the extraction. SEM and XRD studies confirmed changes in the microstructure of the date pulp biomatrix before and after pretreatment. Therefore, the consecutive approach of Sc-CO2 conditioning followed by aqueous extraction appears to be an efficient and promising technique for the enhanced recovery of fruit sugars at relatively low temperatures and without the use of any toxic solvents.