2D MXenes for controlled releases of therapeutic proteins.

MXenes belong to a new class of two dimensional (2D) functional nanomaterials, mainly encompassing transition-metal carbides, nitrides and carbonitrides, with unique physical, chemical, electronic and mechanical properties for various emerging applications across different fields. To date, the potentials of MXenes for biomedical application such as drug delivery have not been thoroughly explored due to the lack of information on their biocompatibility, cytotoxicity and biomolecule-surface interaction. In this study, we developed novel drug delivery system from MXene for the controlled release of a model therapeutic protein. First, the structural, chemical and morphological properties of as synthesized MXenes were probed with electron microscopy and X-ray diffraction. Second, the potential cytotoxicity of MXene toward the proliferation and cell morphology of murine macrophages (RAW 264.7) were evaluated with MTT assays and electron microscopy, respectively. Moreover, the drug loading capacities and sustained release capabilities of MXene were assessed in conjunction with machine learning approaches. Our results demonstrated that MXene did not significantly induce cellular toxicity at any concentration below 1 mg/ml which is within the range for effective dose of drug delivery vehicle. Most importantly, MXene was efficiently loaded with FITC-catalase for subsequently achieving controlled release under different pHs. The release profiles of catalase from MXene showed higher initial rate under basic buffer (pH 9) compared to that in physiological (pH 7.4) and acidic buffers (pH 2). Taken together, the results of this study lead to a fundamental advancement toward the use of MXene as a nanocarrier for therapeutic proteins in drug delivery applications.

Data-driven prediction of in situ CO2 foam strength for enhanced oil recovery and carbon sequestration.

Carbon dioxide foam injection is a promising enhanced oil recovery (EOR) method, being at the same time an efficient carbon storage technology. The strength of CO2 foam under reservoir conditions plays a crucial role in predicting the EOR and sequestration performance, yet, controlling the strength of the foam is challenging due to the complex physics of foams and their sensitivity to operational conditions and reservoir parameters. Data-driven approaches for complex fluids such as foams can be an alternative method to the time-consuming experimental and conventional modeling techniques, which often fail to accurately describe the effect of all important related parameters. In this study, machine learning (ML) models were constructed to predict the oil-free CO2 foam apparent viscosity in the bulk phase and sandstone formations. Based on previous experimental data on various operational and reservoir conditions, predictive models were developed by employing six ML algorithms. Among the applied algorithms, neural network algorithms provided the most precise predictions for bulk and porous media. The established models were then used to compute the critical foam quality under different conditions and determine the maximum apparent foam viscosity, effectively controlling CO2 mobility to co-optimize EOR and CO2 sequestration.