A dielectrophoresis-based microfluidic chip for trapping circulating tumor cells using a porous membrane

Circulating tumor cells (CTCs) have been widely considered as novel biomarkers for clinical diagnosis of cancer. CTCs are the cells detached from the parent tumors and shed into the blood stream to initiate tumor metastasis. Although CTCs are rare, their detection in one’s blood sample is essential for cancer early diagnosis and for starting the treatment procedure. Here, we introduce a novel method for trapping CTCs using dielectrophoresis (DEP), which effectively employs pores of a replaceable porous membrane as CTC traps. The applied dielectrophoretic force efficiently traps and holds CTCs in a stable position and further enables us to perform various on chip analysis on them. First, using finite element method, the performance of the system was simulated for different physical conditions. Then, the chip was fabricated and its trapping performance was experimentally validated. Cells were entered into the microchannel and trapped in the pores of a polydimethylsiloxane (PDMS) membrane. The proposed microfluidic chip is capable of detecting rare cells in a large cell population.

Entomological Survey Confirms Changes in Mosquito Composition and Abundance in Senegal and Reveals Discrepancies among Results by Different Host-Seeking Female Traps

Mosquitoes-borne diseases are major public health issues particularly in Africa. Vector control interventions and human-made environmental/climatic changes significantly affect the distribution and abundance of vector species. We carried out an entomological survey targeting host-seeking mosquitos in two different ecological contexts—coastal and inland—in Senegal, by CDC-light and BG-sentinel traps. Results show high predominance of Culex quinquefasciatus (90%) and of Anopheles arabiensis within malaria vectors (46%), with mean numbers of females/trap/nights =8 and <1, respectively, reinforcing previous evidence of changes in species composition and abundance, highlighting thus increasing risk of transmission of filariasis and emerging arboviruses in the Senegambia region. From the methodological perspective, results show a higher specificity of BG traps for Cx. quinquefasciatus and of CDC traps for An. gambiae s.l. and highlight that, despite both traps target the host-seeking fraction of the population, they provide different patterns of species abundance, temporal dynamics and host-seeking activity, leading to possible misinterpretation of the species bionomics. This draws attention to the need of taking into account trapping performance, in order to provide realistic quantification of the number of mosquitoes per units of space and time, the crucial parameter for evaluating vector–human contact, and estimating risk of pathogen transmission.

Topology Optimization of Passive Cell Traps

This paper discusses a flexible design method of cell traps based on the topology optimization of fluidic flows. Being different from the traditional method, this method obtains the periodic layout of the cell traps according to the cell trapping requirements by proposing a topology optimization model. Additionally, it satisfies the cell trapping function by restricting the flow distribution while taking into account the overall energy dissipation of the flow field. The dependence on the experience of the designer is reduced when this method is used to design a cell trap with acceptable trapping performance. By comparing the influence of the changes of various parameters on the optimization results, the flexibility of the topology optimization method for cell trap structure optimization is verified. The capability of this design method is validated by several performed comparisons between the obtained layouts and optimized designs in the published literature.

Developing machine learning models to predict CO2 trapping performance in deep saline aquifers

Deep saline formations are considered as potential sites for geological carbon storage (GCS). To better understand the CO2 trapping mechanism in saline aquifers, it is necessary to develop robust tools to evaluate CO2 trapping efficiency. This paper introduces the application of Gaussian process regression (GPR), support vector machine (SVM), and random forest (RF) to predict CO2 trapping efficiency in saline formations. First, the uncertainty variables, including geologic parameters, petrophysical properties, and other physical characteristics data were utilized to create a training dataset. A total of 101 reservoir simulation samples were then performed, and the residual trapping, solubility trapping, and cumulative CO2 injection were collected. The predicted results indicate that three machine learning (ML) models that evaluate performance from high to low: GPR, SVM, and RF can be selected to predict the CO2 trapping efficiency in deep saline formations. The GPR model has an excellent CO2 trapping prediction efficiency with the highest correlation factor (R2 = 0.992) and lowest root mean square error (RMSE = 0.00491). The accuracy and stability of the GPR models were verified for an actual reservoir in offshore Vietnam. The predictive models obtained a good agreement between the simulated field and the predicted trapping index. These findings indicate that the GPR ML models can support the numerical simulation as a robust predictive tool for estimating the performance of CO2 trapping in the subsurface.

Modeling of CO2-LPG WAG with asphaltene deposition to predict coupled enhanced oil recovery and storage performance

Combined carbon capture and storage and CO2-enhanced oil recovery (CCS-EOR) can reconcile the demands of business with the need to mitigate the effects of climate change. To improve the performance of CCS-EOR, liquefied petroleum gas (LPG) can be co-injected with CO2, leading to a reduction in the minimum miscibility pressure. However, gas injection can cause asphaltene problems, which undermines EOR and CCS performances simultaneously. Here, we systematically examine the mechanisms of asphaltene deposition using compositional simulations during CO2-LPG–comprehensive water–alternating-gas (WAG) injection. The LPG accelerates asphaltene deposition, reducing gas mobility, and increases the performance of residual trapping by 9.2% compared with CO2 WAG. In contrast, solubility trapping performance declines by only 3.7% because of the greater reservoir pressure caused by the increased formation damage. Adding LPG enhances oil recovery by 11% and improves total CCS performance by 9.1% compared with CO2 WAG. Based on reservoir simulations performed with different LPG concentrations and WAG ratios, we confirmed that the performance improvement of CCS-EOR associated with increasing LPG and water injection reaches a plateau. An economic evaluation based on the price of LPG should be carried out to ensure practical success.

Influence of magnetic field and working voltage on the tripping performance in spherical cylindrical ECP

In order to further improve the trapping effect of fine particles, a new electrostatic cyclone precipitator (ECP) with magnetic confinement was proposed, the overall efficiencies of fine particles under different operating conditions were numerically simulated, and the influence of working voltage on the dust-removal effect of fine particles with and without magnetic field were discussed. The results show that increasing working voltage or magnetic induction intensity improves the trapping performance of spherical cylindrical magnetically confinement ECP, and the lifting effect gradually weakens while increasing the same amplitude. The results can offer technical reference for the optimization design of greatly improving the ECP dust-removal performance.