Low-cost polyamide thin-film composite (TFC) membranes are being explored as alternatives to cation exchange membranes for seawater electrolysis. An optimal membrane should have a low electrical resistance to minimize applied potentials needed for water electrolysis and be able to block chloride ions present in a seawater catholyte from reaching the anode. The largest energy loss associated with a TFC membrane was the Nernstian overpotential of 0.74 V (equivalent to 37 Ω cm² at 20 mA cm^-2), derived from the pH difference between the anolyte and catholyte and not the membrane ohmic overpotential. Based on analysis using electrochemical impedance spectroscopy, the pristine TFC membrane contributed only 5.00 Ω cm² to the ohmic resistance. Removing the polyester support layer reduced the resistance by 79% to only 1.04 Ω cm², without altering the salt ion transport between the electrolytes. Enlarging the pore size (∼5 times) in the polyamide active layer minimally impacted counterion transport across the membrane during electrolysis, but it increased the total concentration of chloride transported by 60%. Overall, this study suggests that TFC membranes with thinner but mechanically strong supporting layers and size-selective active layers should reduce energy consumption and the potential for chlorine generation for seawater electrolyzers.

Oceans are crucial to human survival, providing natural resources and most of the global oxygen supply, and are responsible for a large portion of worldwide economic development. Although it is widely considered a silent world, the sea is filled with natural sounds generated by marine life and geological processes. Man-made underwater sounds, such as active sonars, maritime traffic, and offshore oil and mineral exploration, have significantly affected underwater soundscapes and species. In this work, we report on a joint optical fiber-based communication and sensing technology aiming to reduce noise pollution in the sea while providing connectivity simultaneously with a variety of underwater applications. The designed multifunctional fiber-based system enables two-way data transfer, monitoring marine life and ship movement near the deployed fiber at the sea bottom and sensing temperature. The deployed fiber is equally harnessed to transfer energy that the internet of underwater things (IoUTs) devices can harvest. The reported approach significantly reduces the costs and effects of monitoring marine ecosystems while ensuring data transfer and ocean monitoring applications and providing continuous power for submerged IoUT devices.

Based on both total energy calculations and comparison of experimental and calculated characteristics of the photoelectron spectrum (PHES), the structural assignment of clusters Agn– (n = 13–16) and Cum– (m = 14–17) has been made using the density functional theory (DFT) model with our previously developed S2LYP functional. A comparative study of size dependence of geometry, electronic structure, and physicochemical properties has been carried out for a series of anionic silver and copper clusters containing up to 20 atoms. For the cases when two isomers contribute to the experimental PHES, the isomerization barriers and molar ratio of isomers were estimated. It has been shown that the geometry and the properties that are determined mainly by ns-derived electronic states are similar for copper and silver clusters. However, due to the larger contribution of (n–1)d-electrons to the chemical bond, the potential energy surface of copper clusters is less smooth, and these clusters are characterized by higher isomerization energies compared to silver clusters. The isomerization energies of clusters and the number of isomers with similar energies increase with enlarging cluster size. Thus, clusters containing less than 20 atoms easily overcome the barriers of intramolecular isomerization (i.e., behave like liquids). However, it is expected that cooled clusters containing several tens of atoms will have a rigid geometry due to high intramolecular isomerization energies.

The sum of the longest diameter (SLD) of the target lesions is a longitudinal biomarker used to assess tumor response in cancer clinical trials, which can inform about early treatment effect. This biomarker is semicontinuous, often characterized by an excess of zeros and right skewness. Conditional two-part joint models were introduced to account for the excess of zeros in the longitudinal biomarker distribution and link it to a time-to-event outcome. A limitation of the conditional two-part model is that it only provides an effect of covariates, such as treatment, on the conditional mean of positive biomarker values, and not an overall effect on the biomarker, which is often of clinical relevance. As an alternative, we propose in this article, a marginalized two-part joint model (M-TPJM) for the repeated measurements of the SLD and a terminal event, where the covariates affect the overall mean of the biomarker. Our simulation studies assessed the good performance of the marginalized model in terms of estimation and coverage rates. Our application of the M-TPJM to a randomized clinical trial of advanced head and neck cancer shows that the combination of panitumumab in addition with chemotherapy increases the odds of observing a disappearance of all target lesions compared to chemotherapy alone, leading to a possible indirect effect of the combined treatment on time to death.

Climate change projections (CCPs) are based on the multimodel means of individual climate model simulations that are assumed to be independent. However, model similarity leads to projections biased toward the largest set of similar models and intermodel uncertainty underestimation. We assessed the influences of similarities in CMIP6 through CMIP3 CCPs. We ascertained model similarity from shared physics/dynamics and initial conditions by comparing simulated spatial temperature and precipitation with the corresponding observed patterns and accounting for intermodel spread relative to the observational uncertainty, which is also critical. After accounting for similarity, the information from 57 CMIP6, 47 CMIP5, and 24 CMIP3 models can be explained by just 11 independent models without significant differences in globally averaged climate change statistics. On average, independent models indicate a lower global-mean temperature rise of 0.25 °C (~0.5 °C–1 °C in some regions) relative to all models by the end of the 21st century under CMIP6’s highest emission scenario.

Energy and environmental concerns are motivating the use of renewable fuels such as methanol. Furthermore, the implementation of the oxy-combustion and hydrothermal combustion technologies can help to improve the performance of power generation and reduce NOx emissions. These aspects can contribute to achieve the transition to cleaner sources of energy that is being sought worldwide, and thus we carried out the first molecular dynamics study of the oxidation of methanol at 2700 K and 3000 K in four supercritical environments with compositions CH3OH + O2, CH3OH + O2+CO2, CH3OH + O2+H2O, and CH3OH + O2+CO2+H2O. Reaction mechanisms were obtained and revealed that the initiation reaction is CH3OH unimolecular dissociation in all cases. The CH3OH oxidation chemistry changes when O2 is replaced by supercritical CO2 (sCO2) and/or H2O (sH2O), and a new route for the important oxidation sequence CH3OH→CH2OH→H2CO→CHO→CO→CO2 is reported. The rate constants for the CH3OH unimolecular dissociation were calculated, indicating a positive effect of sH2O. Furthermore, the collisions of CH3OH molecules with those of H2O and CO2 were analyzed with molecular dynamics simulations and quantum chemistry calculations, suggesting that collisions with H2O can activate more efficiently CH3OH for a prospective dissociation event. This study is aimed to help in the development of kinetic models for CH3OH oxidation/pyrolysis in sCO2 and sH2O, and thus in the implementation of the oxy-combustion and hydrothermal combustion techniques for this alternative fuel.

FMS-like tyrosine kinase 3 (FLT3) mutations occur in almost 30% of acute myeloid leukemia (AML) patients. Despite the initial clinical efficacy of FLT3 inhibitors, many treated AML patients with mutated FLT3 eventually relapse. This review critically discusses the potential and challenges of FLT3-targeted therapies and sheds light on their drug interactions as well as potential biomarkers. Furthermore, we focus on the molecular mechanisms underlying the resistance of FLT3 internal tandem duplication (FLT3-ITD) AMLs to FLT3 inhibitors alongside novel therapeutic strategies to reverse resistance. Notably, dynamic heterogeneous patterns of clonal selection and evolution contribute to the resistance of FLT3-ITD AMLs to FLT3 inhibitors. Ongoing preclinical research and clinical trials are actively directed towards devising rational "personalized" or "patient-tailored" combinatorial therapeutic regimens to effectively treat patients with FLT3 mutated AML.

Bioflocculants can be used for cost-effective harvesting of microalgae biomass on an industrial scale. This study investigates the flocculation-based harvesting approach to recovering Chlorella vulgaris microalgae biomass using chitosan biopolymer. Response surface methodology (RSM) was used to design the experiments and optimize the critical operating parameters. Box-Behnken Design (BBD) was employed at three levels, and 17 experimental runs were conducted to determine the optimal conditions and the relationship between operating parameters. The highest biomass recovery of 99.10% was achieved at the following optimized conditions: pH of 5, flocculation time of 45 min, and chitosan concentration of 10 mg/L. Both experimental results and model outputs indicated that pH significantly impacts microalgae harvesting and that process performance is less dependent on chitosan concentration and flocculation time. The quadratic model has shown the best fit with the experimental results. The results could be applied to large-scale microalgae harvesting applications to promote microalgae biomass recovery and reduce operating costs.

The last decade has witnessed considerable progress in underwater wireless optical communication in complex environments, particularly in exploring the deep sea. However, it is difficult to maintain a precise point-to-point reception at all times due to severe turbulence in actual situations. To facilitate efficient data transmission, the color-conversion technique offers a paradigm shift in large-area and omnidirectional light detection, which can effectively alleviate the étendue limit by decoupling the field of view and optical gain. In this work, we investigated a series of difluoroboron β-diketonate fluorophores by measuring their photophysical properties and optical wireless communication performances. The emission colors were tuned from blue to green, and >0.5 Gb/s data transmission was achieved with individual color channel in free space by implementing an orthogonal frequency-division multiplexing (OFDM) modulation scheme. In the underwater experiment, the fluorophore with the highest transmission speed was fabricated into a 4×4 cm2 luminescent concentrator, with the concentrated emission from the edges coupled with an optical fiber array, for large-area photodetection and optical beam tracking. The net data rates of 130 Mb/s and 217 Mb/s were achieved based on nonreturn- to-zero on-off keying and OFDM modulation schemes, respectively. Further, the same device was used to demonstrate the linear light beam tracking function with high accuracy, which is beneficial for sustaining a reliable and stable connection in a dynamic, turbulent underwater environment.

The fast marching method (FMM) is a highly efficient numerical algorithm used to solve the Eikonal equation. It calculates traveltime from the source point to different spatial locations and provides a geometric description of the advancing front in anisotropic and heterogeneous media. As the Eikonal solution, the diffusive time of flight (DTOF) can be used to formulate an asymptotic approximation to the pressure diffusivity equation to describe transient flow behavior in subsurface porous media. For the infinite-acting flow that occurs in porous media with smoothly varying heterogeneity, traveltime of the pressure front from the active production or injection well to the observation well can be directly estimated from the DTOF using the concept of radius (or depth) of investigation (ROI or DOI), which is defined as the moment when a maximum magnitude of the partial derivative of pressure to time occurs. Based on the ROI or DOI definition, we propose a deep neural network called the inversion neural network (INN) to inversely estimate heterogeneous reservoir permeability by inverting the traveltime data. The INN is trained by traveltime data created for a large data set of distinct permeability fields from FMM simulations, which can be two orders of magnitude faster than conventional reservoir simulators. A convolutional neural network (CNN), the U-Net architecture, is incorporated into the INN, which establishes a nonlinear mapping between the heterogeneous permeability fields and the traveltime data collected at sparse observation wells. The loss function used for the INN is defined as the root mean square error (RMSE) between the logarithm of the predicted permeability and the logarithm of the true permeability. The performance of the INN is tested on reservoir models with both smoothly varying heterogeneity and high-contrast media properties. For the 2D smoothly varying heterogeneous models with a grid size of 49×49, the permeability predicted by the INN has an average estimation error of 8.73% when a set of 7×7 uniformly distributed observation wells is used to collect “observational” traveltime data from the FMM simulation. For models with the same grid size and observation well density but with high-contrast media properties, the INN can still capture the general heterogeneity distribution, although with reduced prediction accuracy. Using a graphics processing unit (GPU) for training and prediction allows the entire inverse modeling process for a 2D 49×49 reservoir model to be completed within 7 minutes.