Column Diameter Research Articles

A Micro-Flow Liquid Chromatography-Mass Spectrometry Method for the Quantification of Oxylipins in Volume-Limited Human Plasma.

Oxylipins are well-known lipid mediators in various inflammatory conditions. Their endogenous concentrations range from low picomolar to nanomolar, and there are growing demands to determine their concentrations in low-volume matrices for pathological studies, including blood, cerebrospinal fluids from animal disease models, infants, and microsampling devices. Most of the published quantification methods for comprehensive profiling of oxylipins still require more than 50µL plasma as a starting volume to detect these low levels. The aim of our study is to develop a sensitive and reliable method for the quantification of oxylipins in volume-limited human plasma samples. We established and validated a micro-liquid chromatography (LC)-mass spectrometry (MS)/MS method that requires only 5µL of human plasma for the determination of 66 oxylipins. The optimized micro-LC-MS/MS method utilized a flow rate of 4µL/min with a 0.3-mm inner diameter column. With an injection volume of 3µL, our method provides limits of detection in the range from 0.1 to 91.9pM, and limits of quantification range from 0.3 to 306.2pM. The sensitivity enhancement compared to conventional flow ranged from 1.4 to 180.7 times for 51 compounds depending on their physical-chemical properties. After validation, the method was applied to analyze 40 plasma samples from a healthy aging study to demonstrate robustness and sensitivity.

Numerical analysis of soil reinforced by sand columns using different installation methods

This study involves a finite element numerical simulation of laboratory tests conducted on small-scale models. The tests involved the installation of columns using different techniques: without soil displacement and without column compaction (WR-NC), and without soil displacement but with column compaction (WR-WC). The reinforced soil masses were then subjected to a uniform 150 kPa load. A comparison between the numerical and experimental results showed excellent agreement, validating the simulation. Following this validation, a parametric analysis was carried out to examine the effects of the various parameters on the behavior of the reinforced soil masses. In the first method, three different column diameters were analyzed, focusing on the effects of diameter (area replacement ratio), column length, and geogrid containment benefits. In the second method, three levels of compaction pressure were applied during the column installation. The study evaluated the impact of column installation techniques on the behavior of the reinforced masses. The findings indicate that settlement reduction is proportional to the increase in area replacement ratio and column length. Additionally, confining the columns with geogrid was shown to be effective in further reducing settlements. The results also revealed that increasing compaction stress leads to greater radial expansion of the columns, which densifies and stiffens the surrounding soil, thereby reducing settlement. Overall, the study concludes that column installation techniques significantly influence the behavior of reinforced soil masses.

Considerations for method development and method translation in capillary liquid chromatography: A tutorial

HPLC remains one of the most widely used measurement techniques for chemical analysis. Capillary LC, which utilizes narrow diameter columns operated at lower flow rates than analytical-scale LC, continues to gain adoption based on its reduced mobile phase consumption and increased sensitivity when coupled to MS detection. This tutorial offers practical insights into the most critical aspects of translating analytical-scale separations to the capillary scale. The selection of pumping systems, detectors, and the potential for performance loss due to extra-column effects are examined within the context of separations using columns with inner diameters ≤ 0.3 mm. Column choices within this diameter range are also detailed, both in terms of stationary phase support options and general commercial availability. The impact of these various factors on the effective development/translation of LC methods down to flow rates under 10 µL/min is described to provide readers with a basis for implementing these strategies within their own analytical workflows.

Multi-objective optimization of a novel gas-liquid cylindrical cyclone based on response surface methodology

Currently, most major oilfields in China have reached a stage of high water content, where the extracted fluid is often accompanied by significant volumes of associated gas. This situation increases extraction costs and elevates safety risks. To address these challenges, this paper presents an innovative redesign of the traditional gas-liquid cylindrical cyclone (GLCC) by applying composite mechanics to improve the inner cone structure. The new design is tailored for conditions with high gas content and flow rates. Using response surface optimization, the optimal structural parameters for the separator were identified: an inlet area of 881 mm2, a column diameter of 110 mm, and an inner cone height of 189 mm. Furthermore, the study integrates numerical simulation with experimental research to compare and analyze the flow characteristics and separation performance of the GLCC before and after optimization. The results demonstrate that the optimized GLCC achieves a separation efficiency of 93.2 %, an improvement of 21.2 percentage points over the initial design, with a maximum pressure loss of 0.0621 MPa. The experimental findings and numerical simulations show strong agreement, confirming the efficacy of the new design.

Development of microflow ultra high performance liquid chromatography-mass spectrometry metabolomic assays for analysis of mammalian biofluids

Introduction and objectivesThe application of untargeted metabolomics assays using ultra high performance liquid chromatography-mass spectrometry (UHPLC-MS) to study metabolism in biological systems including humans is rapidly increasing. In some of these studies there is a requirement to collect and analyse low sample volumes of biofluids (e.g. tear fluid) or low cell and tissue mass samples (e.g. tissue needle biopsies). The application of microflow, capillary or nano liquid chromatography (≤ 1.0 mm column internal diameter (i.d.)) theoretically should accomplish a higher assay sensitivity compared to analytical liquid chromatography (2.1–5.0 mm column internal diameter). To date, there has been limited research into microflow UHPLC-MS assays that can be applied to study samples of low volume or mass.MethodsThis paper presents three complementary UHPLC-MS assays (aqueous C18 reversed-phase, lipidomics C18 reversed-phase and Hydrophilic Interaction Liquid Chromatography (HILIC)) applying 1.0 mm internal diameter columns for untargeted metabolomics. Human plasma and urine samples were applied for the method development, with porcine plasma, urine and tear fluid used for method assessment. Data were collected and compared for columns of the same length, stationary phase and stationary phase particle size but with two different column internal diameters (2.1 mm and 1.0 mm).Results and conclusionsAll three assays showed an increase in peak areas and peak widths when applying the 1.0 mm i.d. assays. HILIC assays provide an advantage at lower sample dilutions whereas for reversed phase (RP) assays there was no benefit added. This can be seen in the validation study where a much higher number of compounds were detected in the HILIC assay. RP assays were still appropriate for small volume samples with hundreds of compounds being detected. In summary, the 1.0 mm i.d. column assays are applicable for small volume samples where dilution is required during sample preparation.

Leveraging the potential of 1.0-mm i.d. columns in UHPLC-HRMS-based untargeted metabolomics.

Untargeted metabolomics UHPLC-HRMS workflows typically employ narrowbore 2.1-mm inner diameter (i.d.) columns. However, the wide concentration range of the metabolome and the need to often analyze small sample amounts poses challenges to these approaches. Reducing the column diameter could be a potential solution. Herein, we evaluated the performance of a microbore 1.0-mm i.d. setup compared to the 2.1-mm i.d. benchmark for untargeted metabolomics. The 1.0-mm i.d. setup was implemented on a micro-UHPLC system, while the 2.1-mm i.d. on a standard UHPLC, both coupled to quadrupole-orbitrap HRMS. On polar standard metabolites, a sensitivity gain with an average 3.8-fold increase over the 2.1-mm i.d., along with lower LOD (LODavg 1.48ng/mL vs. 6.18ng/mL) and LOQ (LOQavg 4.94ng/mL vs. 20.60ng/mL), was observed. The microbore method detected and quantified all metabolites at LLOQ with respect to 2.1, also demonstrating good repeatability with lower CV% for retention times (0.29% vs. 0.63%) and peak areas (4.65% vs. 7.27%). The analysis of various samples, in both RP and HILIC modes, including different plasma volumes, dried blood spots (DBS), and colorectal cancer (CRC) patient-derived organoids (PDOs), in full scan-data dependent mode (FS-DDA) reported a significant increase in MS1 and MS2 features, as well as MS/MS spectral matches by 38.95%, 39.26%, and 18.23%, respectively. These findings demonstrate that 1.0-mm i.d. columns in UHPLC-HRMS could be a potential strategy to enhance coverage for low-amount samples while maintaining the same analytical throughput and robustness of 2.1-mm i.d. formats, with reduced solvent consumption.

Residual behaviour and damage assessment of UHPC-filled double-skin steel tubular columns after lateral impact

Ultra-high performance concrete (UHPC)-filled double-skin steel tubular (DST) column has great potential to be used in the protective structures. Although its lateral impact behaviour has been well understood, the residual behaviour after lateral impact remains unexplored. As a result, this study extensively investigated the residual behaviour and damage assessment of UHPC-filled DST columns after lateral impact. Firstly, a set of six DST columns were designed and tested under lateral impact, followed by static axial compression. In addition, two intact columns were subjected to static axial compression for comparative analysis. Secondly, the refined finite element models were developed and validated using the current test data, and the impact resistant mechanism of UHPC-filled DST columns with different impact locations was analysed. Thirdly, the suitability of different damage indexes for the damage assessment of impacted UHPC-filled DST columns was evaluated. Two damage indexes, the ratio of mid-height deflection to column height (R1), and the ratio of local deflection to the column diameter (R2), were proposed for the DST columns. Finally, two types of machine learning-based models were developed to predict the impact damage of UHPC-filled DST columns. The prediction models were interpreted locally and globally using the additive feature attribution method Shapley Additive Explanation (SHAP). The machine learning-based prediction models can rapidly evaluate the damage extent of impacted UHPC-filled DST column, which hold great significance for the selection of strengthening and retrofitting schemes.

MODELLING GBM RECURRENCE: THE INflUENCE OF PERI-LESIONAL OEDEMA AND THE DISCONNECTOME

Abstract AIMS Residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker, often involving the area of T2 signal elevation surrounding the tumour core, histopathologically representing infiltrative oedema with tumour cells. We present the first stage of a model to improve the prediction GBM recurrence by specifically investigating the peri-lesional oedema compartment. METHOD UK-wide sample of histologically proven IDHwt GBMs with MRIs at the time of diagnosis underwent state-of- the-art in-house tumour segmentation pipeline and radiomic feature extraction of whole-tumour, enhancing, non-enhancing and oedema components, and masked disconnectome map components. Of these, early GBM recurrence, within 6 months after completion of radiotherapy, was curated and distribution mapped across the brain. RESULTS 1464 patients were identified: male = 901, median age 63 years, average overall survival 597 days. The radiomic features: Compactness 1 and 2, Surface Area, Spherical Disproportion, Sphericity, and Maximum 2D Diameter column of the disconnectome-related perilesional oedema compartment were most predictive of survival outcome (p value <0.01). Of these, 47 patients with average overall survival 357 days and average time from end of radiotherapy to progression of 131 days were identified. CONCLUSION Our early results reveal the relationship between perilesional oedema MRI features and survival, with disconnectome-related perilesional oedema features most predictive. This suggests that extended brain network aberrations caused by gliomas may also influence tumour recurrence. Further work will focus on the development of more robust predictive models of tumour recurrence that have potential for significant impact on surgical and radiotherapy planning.

Stability analysis of CO2 microbubble for CO2 sequestration and mobility control in enhanced oil recovery

This study addresses the challenge of CO2 microbubble stability in enhanced oil recovery (EOR), with a focus on the combined effects of surfactant concentration, brine concentration, brine-oil ratio, and gas flow rate. Microbubble destabilization, driven by phase separation and improper brine-oil ratios, is a critical issue in EOR. The novelty of this research lies in the use of Acacia concinna, a natural, non-ionic surfactant, to enhance microbubble stability sustainably. The study systematically investigates foamability, drainage kinetics, surface tension, rheology, and bubble size distribution to assess microbubble stability. The results showed that increasing surfactant concentration and brine-oil ratio significantly impacted foam stability and bubble size distribution, with optimal stability conditions observed at 2 wt% surfactant concentration (53.6 min), 5000 ppm brine concentration (47.6 min), a brine-oil ratio of 1:2 (52.4 min), and a CO2 gas flow rate of 1 lpm. Key findings also indicate that increasing NaCl concentration from 5000 to 20,000 ppm caused an increase in microbubble diameter, while higher gas flow rates reduced bubble size. The work introduces new empirical correlations for predicting bubble size distribution based on column diameter, surfactant and brine concentrations, viscosity, density, and gas superficial velocity. These correlations provide a more accurate means of optimizing EOR processes, improving oil recovery efficiency while enhancing CO2 sequestration, contributing to both energy production and environmental sustainability.

Development of a selective sequential process for H2S enrichment and CO2 capture in aqueous MDEA solutions

The typical acid gas removal process can simultaneously remove acid gases from the raw gas using an aqueous MDEA solution. But it cannot further separate and capture hydrogen sulfide and carbon dioxide, and increases the burden on subsequent processing. Recovering carbon dioxide from that not only increases the H2S content in the acid gas, but also achieves the CO2 capture. To realize these, acid gas enrichment and carbon dioxide capture are integrated into the typical acid gas removal process. In this study, a novel selective sequential process in aqueous MDEA solutions is proposed for H2S enrichment and CO2 capture. And an optimization framework is proposed to optimize the parameters of the whole process, mainly to improve the efficiency of the column equipment. It is found that small diameter column is suitable for enhancing selectivity and large diameter column is suitable for maximizing productive capacity. To demonstrate the performance of the proposed process, the evaluation results, carbon sulfur ratio (3.81), return on investment (6.26) and carbon dioxide capture ratio (−0.63) are obtained from technical, economic and environmental evaluations. In comparison with the typical process, the novel process can achieve H2S enrichment and CO2 capture while remaining economically viable. This study presents a method for collaborative removal of H2S and CO2 in acid gas removal process. It is advantageous to reduce carbon dioxide emissions.

Effect of Pouring Techniques and Funnel Structures on Crucible Metallurgy: Physical and Numerical Simulations

In the planar flow casting process of amorphous strips, the flow behavior of molten metal and the inclusion content in the crucible are crucial to the morphology and magnetic properties of the material. This study conducts a comparative analysis of the effects of non-immersed and immersed funnels, as well as various funnel structures, on the fluid flow and inclusion removal efficiency in the crucible by integrating numerical and physical models. The findings reveal that for the same pouring flow rate, the diameter of the liquid column in non-immersed pouring conditions is smaller than that of the funnel outlet, leading to a faster injection flow velocity. As a result, the melt in the crucible is subjected to severe impacts, accompanied by an increased possibility of slag entrapment. Conversely, immersed pouring substantially reduces the velocity of the molten metal at the funnel outlet, thereby extending the residence time in the crucible and diminishing the volume of the dead zone. Additionally, the molten metal backflows due to the negative pressure formed in the inner chamber of the funnel. The design of a trumpet-shaped funnel increases the effective volume while reducing the height of the backflow fluid, consequently reducing the velocity of the molten metal at the funnel outlet and prolonging the residence time. Compared to the conventional pouring process with the non-immersed funnel, the outlet velocity is reduced from 1.1 m/s to 0.12 m/s by adopting the immersed funnel with an inverted trapezoidal trumpet structure. This reduction results in a stable flow state, a 9.69% reduction in the dead zone volume fraction, and a 22.96% increase in average inclusion removal efficiency. These improvements demonstrate that a crucible funnel with a well-designed structure and the implementation of an immersion process can significantly improve the metallurgical effects in the planar flow casting process.

Homogeneous reactive distillation: The influence of pressure drop on separation and reaction kinetics

This study investigates the Total Annual Cost (TAC) optimization of a reactive distillation (RD) process for methyl acetate (MA) production as a function of tray pressure drop, focusing on the trade-off between chemical reaction kinetics and separation efficiency. The analysis examines how variations in column diameter and weir height influence the TAC across different liquid hold-ups with and without incorporating a tray pressure drop in the simulations. Results indicate that for larger liquid hold-ups, the decrease in separation efficiency predominates over kinetic advantages, leading to higher energy costs when pressure drop is considered. Despite the loss in separation efficiency with a high weir height, a pressure drop-incorporated column design featuring a high weir height and small diameter is shown to achieve lower TAC due to the comparatively lower installed costs. A reduction of tray liquid hold-up of more than 50 % is found for the optimal column design in the considered case study when the tray pressure drop is considered. A sensitivity analysis towards a hypothetical infinite fast reaction is conducted to illustrate how the energy costs are affected by the kinetic rate of the chemical reaction.

Estimation of gas hold-up in bubble columns using wall pressure fluctuations and machine learning

We present a novel approach to estimate gas hold-up in bubble columns using wall pressure fluctuations – without requiring knowledge of operating flow regime, column configuration or physical properties. The approach uses machine learning and pressure fluctuations acquired from a wall mounted pressure sensor. The approach is validated by carrying out experiments with different physical properties of liquid (deionised water, deionised water – alcohol [ethanol, propanol and butanol] mixture up to 2 % alcohol, deionised water – glycerine (30 %), tap water). The majority of the data was obtained with 0.1 m diameter bubble column. Data with tap water in this column was obtained for four different spargers. Data with tap water was also obtained for 0.15 m and 0.33 m diameter bubble columns. Gas velocity was varied up to 0.1 m/s. The covered physical properties, sparger configurations, column diameter and gas velocities span different flow regimes. An artificial neural network (ANN) based model was developed to relate characteristics of wall pressure fluctuations and superficial gas velocity to the gas hold-up. The approach was validated by comparing predicted results with the experimental data unseen by the ANN model. The approach demonstrates the feasibility of using wall pressure fluctuations with machine learning to estimate key characteristics without prior knowledge of column configuration, flow regimes or physical properties.

Rigorous design and economic optimization of reactive distillation column considering real liquid hold-up and hydraulic conditions of industrial device

The liquid hold-up in a reactive distillation (RD) column not only has a significant impact on the extent of reactions, but also affects the pressure drop and hydraulic conditions in the column. Therefore, the liquid hold-up would be a critical design factor for RD columns. However, the existing design methods for RD columns typically neglect the influence of considerable amount of liquid hold-up in downcomers owing to the difficulties of solving a large-scale nonlinear model system by considering downcomer hydraulics, resulting in significant deviations from actual situation and even operation infeasibility of the designed column. In this paper, a pseudo-transient (PT) RD model based on equilibrium model considering tray hydraulics was established for rigorous simulation and optimization of RD plate columns considering the liquid hold-up both in downcomers and column trays, and a steady-state optimization algorithm assisted by the PT model was adopted to robustly solve the optimization problem. The optimization results of either ethylene glycol RD or methyl acetate RD demonstrated that assuming all the liquid hold-up of a stage belonged to the tray will cause significant deviations in the column diameter, weir height, and the number of stages, which leads to not meeting the separation requirements and even operation hydraulic infeasibility. The rigorous model proposed in this study which considers the liquid hold-up both on trays and in downcomers as well as hydraulic constraints can be applied to systematically design industrial RD plate columns to simultaneously obtain optimal operating variables and equipment structure variables.

Functionalization of High-Density Polyethylene Capillaries for Open Tubular Ion Chromatography.

Ion chromatography is the anion analysis benchmark. A miniature form, Open Tubular Ion Chromatography (OTIC), has attractive attributes for efficient ion separations. Here, we fabricate and characterize high-density polyethylene (HDPE) open tubular anion exchange columns (OTCs). We attach positively charged latex particles onto negatively charged capillary surfaces. For efficient OTIC, column diameters need to be < ∼20µm; functionalizing the bore is challenging. Methods to introduce acid groups to an HDPE capillary bore, e.g., sulfonation using chlorosulfonic or sulfuric acid solutions, with or without grafting of an aromatic ring through photo- or chemical grafting first, are explored. Following quaternary ammonium latex attachment, the ion exchange capacity and separating abilities of each OTC were measured as an index of OTC performance. Gradual loss of capacity was observed for many of these; high-resolution mass spectrometry confirmed the leaching of detached oxidized/sulfonated oligomeric fragments and consequent poisoning of the latex sites. Ways to ameliorate this and/or to rejuvenate the columns are also described.

Loading Properties of Capillary Porous-Layer Columns of Different Diameters with a Silica Gel Sorbent

Loading Properties of Capillary Porous-Layer Columns of Different Diameters with a Silica Gel Sorbent

Stone column strategies for mitigating liquefaction-induced uplift of tunnels

Tunnels embedded in liquefiable soil are frequently subjected to uplift and sustain serious damage during major earthquakes. Mitigation methods to prevent the flotation of these tunnels must be developed and implemented in the natural environment. For this purpose, this study proposes a new method that uses stone columns to enhance soil drainage and mitigate soil liquefaction around tunnels. A circular tunnel in liquefied soil is simulated using a 2D finite element model, PLAXIS 2D, and subjected to a sinusoidal input motion. The tunnel's pre-construction and post-construction scenarios are examined. Parametric studies are carried out to investigate the effect of changing several parameters, such as the distance between stone columns and tunnel springing, the diameter of stone columns, the spacing between stone columns, the number of stone column rows, and the stone column arrangement patterns on the effectiveness of liquefaction mitigation. The study reveals that liquefaction mitigation is enhanced by using stone columns closer to tunnel springing, with larger diameters, less spacing, and more rows of stone columns that are arranged in a square pattern. It also emphasizes the importance of timely implementation of stone columns for maximum benefit.

Dynamics of femtosecond laser-driven liquid jets

The complex dynamics of flowing liquids under external stimuli can lead to significant changes in the direction of liquid jets. In this study, we utilized a femtosecond laser to drive a flowing liquid column and analyzed the effect of varying laser energies on the direction of the liquid flow. Additionally, we examined how the diameter and volume of the liquid column influenced its directional behavior when driven by the laser. By comparing the driving angles under different conditions, we explored the principles governing the femtosecond laser-driven liquid columns. These findings offer valuable insights into the field of liquid dynamics, showcasing femtosecond lasers as a good method for controlling liquid flows.

Experimental Study and Neural Network Predictions of Early-Age Behavior of Microexpansion Concrete in Large-Diameter Steel Tube Columns

The improved mechanical performance of concrete-filled steel tube (CFST) components has led to their widespread application in megastructures. Therein, CFST hybrid arch bridge has become an optimal selection of large span bridges. Nonetheless, for massive concrete poured in CFST members with large diameter, the thermal cracking and sustained rising temperature caused by early hydration heat of core concrete are urgently required to be studied. The two large-diameter CFST columns in this work, each having a diameter of 2.1 m, had their temperature and strain fields recorded in-situ. An existing CFST arch bridge served as the model for the two CFST columns’ design. Additionally, early-age characteristics of several scaled CFST columns with varying diameters were documented. A multi-field finite element (FE) model that combines linked chemical (hydration), heat, and mechanical fields is suggested in order to properly characterize the evolutions of temperature and strain fields. The model is validated by comparing the in-situ measurements with the numerical results. Finally, to investigate the affect factors on the hydration temperature of core concrete in CFST columns, early-age hydration behaviors of CFST columns was simulated using the validated FE models input parameters as water to cement ratio (w/c), cement dosage, heat release of cement and diameter of CFST columns. Based on the numerical results under the input parameters mentioned, the LSTM neural network was constructed, and the hydration temperature variances computed by FE models were selected as the input dataset. Afterwards, the temperature variance of core concrete of CFST columns was predicted using the established LSTM network. It is discovered that the LSTM neural network that was previously constructed was able to predict the peak temperature of CFST columns as well as the hydration temperature of CFST specimens with respect to time.

Predicting the compressive strength of CFRP-confined concrete using deep learning

The application of externally bonded fiber-reinforced polymers (FRP) has revolutionized structural rehabilitation, offering cost-effective and rapid solutions for damaged structures. This study presents a novel deep learning approach for predicting the compressive strength of circular carbon-FRP-confined concrete columns. Leveraging an extensive database of 664 experimental results, the model incorporates monotonicity and smoothness constraints, with hyperparameters optimization using the OPTUNA framework. The proposed model demonstrates exceptional accuracy, achieving R² = 0.93, a20-index = 0.95, and MAPE = 7.89 % on unseen test data, consistently outperforming nine benchmark models including established design guidelines. Scenario-based analysis confirms the model's ability to capture known physical behaviors, such as the effects of concrete strength, column diameter, and FRP thickness on confinement effectiveness. The integration of physical constraints enhances the model's reliability and interpretability, bridging the gap between data-driven and physics-based approaches. This research contributes to the advancement of more accurate, economical, and reliable design guidelines for FRP-strengthened structures, while also demonstrating the potential of constrained deep learning in structural engineering applications.