Robotic applications in high-temperature environments demand flexible tactile sensors that can endure extreme heat. Conventional sensors, typically made of polymers and carbon-based materials, deteriorate quickly under such conditions. This work introduces a novel piezoresistive textile for stable tactile sensing above 495°C. The exceptional durability and heat resistance come from the robust core-shell design of the materials, featuring a silicon oxycarbide core and an amorphous carbon shell, which demonstrates unparalleled mechanical strength and flame resistance. With airflow-assisted rotary spinning and density-controlled sintering, the piezoresistive textile is scalable and consistently performs at temperatures up to 250°C for over 24 h and can withstand even higher temperatures of 495°C for 4 h. When integrated into a robotic gripper, the ultrahigh temperature textile sensor successfully retrieves a small item from flames fueled by alcohol, showcasing its effectiveness in high-temperature tactile sensing. This innovative textile sensor offers a promising solution for tactile sensing and robotic applications in high-temperature environments.

Capillary heterogeneity has been identified over the last decade as a key control on subsurface CO2 flow behavior during geological CO2 sequestration. These heterogeneities can be formed in all sedimentary rocks, ranging from slight variations in the sand grain sizes to extensive sequences of interbedded sands, shales, and limestones. Capillary heterogeneity has been largely, although not entirely, overlooked in subsurface flow modeling because it is assumed to only directly influence fluid redistribution over scales of centimeters to meters. However, even small-scale fluid movements can result in dramatic impacts on the mobility and trapping of CO2 over kilometers. Therefore, neglecting capillary heterogeneity at multiple scales could potentially lead to errors in modeling and predicting field-scale plume migration. In this review paper, we aim to provide a consistent overview to (1) establish that capillary heterogeneity can have a major impact on CO2 plume migration, (2) establish the respective length scales at which capillary heterogeneity matters, and (3) provide guidance for numerical modeling.This review covers pertinent literature and extracts key observations from core to field scales. Experimental studies have shown that millimeter-decimeter scale capillary heterogeneity can cause the so-called capillary heterogeneity trapping in addition to pore-scale residual trapping. Even at such a small scale, capillary heterogeneity can already lead to complex upscaled constitutive relationships, such as flow-rate dependent and anisotropic relative permeability, which affects field-scale CO2 migration even when field-scale heterogeneities are present. Under gravity-dominated flow regimes, centimeter-meter scale capillary heterogeneity can entrap a significant amount of CO2 at the field scale, not only after imbibition but also during drainage. In certain cases, the presence of capillary heterogeneity can even completely stop the vertical movement of the CO2 plume, hence greatly reducing leakage risks. At the meter-kilometer scale, the influence of capillary heterogeneity is more pronounced and can hinder or redirect CO2 migration in both lateral and vertical directions.The impact of capillary heterogeneity across multiple spatial scales poses a great challenge in modeling CO2 migration at the field scale, because it is practically impossible to build a field-scale earth model with grid blocks at the millimeter scale. We recommend a hierarchical modeling approach to address this challenge. At the field scale, earth models are built to capture geological features and heterogeneities in high but still practical grid resolutions. For each facies or rock type in the field-scale model, high-resolution meter-scale “conceptual” models are built using millimeter-scale grid blocks to capture representative fine-scale bedding geometries and heterogeneities in various depositional environments, thereby bridging the gap between the subcore scale and the size of a field-scale simulation grid block. Upscaling is then used to preserve the smaller-scale flow dynamics of various rock types in field-scale simulations. Future work is needed to (1) refine, improve, and validate the hierarchical modeling approach; (2) build libraries of fine-scale bedding models for facies in various environments of deposition; (3) quantify multiscale capillary heterogeneity effects under subsurface uncertainties; (4) gain learning from different storage formations; and (5) establish best practices that balance accuracy and computational speed.

Tremendous efforts have been dedicated to harvesting energy from the ubiquitous natural water evaporation process. Inspired by the plant's bioelectric phenomenon during the transport of sap from bottom to top, the hierarchically porous cellulosic wood was engineered as the robust carrier for efficiently capturing and transferring water and moisture by removing lignin and hemicellulose, releasing the microscale pore in the cell wall and nanoscale pores in the middle lamella. A conductive polymer, poly(3,4-ethylenedioxythiophene)/poly (styrenesulfonate) (PEDOT/PSS), was impregnated in the cellulosic wood, which was further lyophilized to increase the specific surface area and improve the charge transport as an evaporation-driven electrical generator (EEG) with high Young's modulus. This EEG produces a sustained voltage of around 385 mV and current over 11 mu A across the porous and conductive cellulosic wood in a saturated NaCl solution. The anisotropic nature of the cellulosic wood leads to a higher output voltage in the growth direction than in the transverse direction. The driving force behind this energy generation is a self-maintained moisture gradient to generate an ion concentration difference between the output electrodes when the system is exposed to water or moisture. Furthermore, the critical factor of energy generation is the coupling between naturally aligned cellulose microfibrils in wood and water molecule. The generated voltage could be increased by elevating the concentration of ions in the aqueous solution and the temperature difference between the output electrodes. These strong and hierarchical cellulosic wood have a significant potential for electric energy collection through water evaporation and waste heat energy.

Predicting terrorism risk is crucial for formulating detailed counter-strategies. However, this task is challenging mainly because the risk of the concerned potential victim is not isolated. Terrorism risk has a spatiotemporal interprovincial contagious characteristic. The risk diffusion mechanism comes from three possibilities: cross-provincial terrorist attacks, internal and external echoes, and internal self-excitation. This study proposed a novel spatiotemporal graph convolutional network (STGCN)-based extension method to capture the complex and multidimensional non-Euclidean relationships between different provinces and forecast the daily risks. Specifically, three graph structures were constructed to represent the contagious process between provinces: the distance graph, the province-level root cause similarity graph, and the self-excited graph. The long short-term memory and self-attention layers were extended to STGCN for capturing context-dependent temporal characters. At the same time, the one-dimensional convolutional neural network kernel with the gated linear unit inside the classical STGCN can model single-node-dependent temporal features, and the spectral graph convolution modules can capture spatial features. The experimental results on Afghanistan terrorist attack data from 2005 to 2020 demonstrate the effectiveness of the proposed extended STGCN method compared to other machine learning prediction models. Furthermore, the results illustrate the crucial of capturing comprehensive spatiotemporal correlation characters among provinces. Based on this, this article provides counter-terrorism management insights on addressing the long-term root causes of terrorism risk and performing short-term situational prevention.

Epidemic spatial-temporal risk analysis, e.g., infectious number forecasting, is a mainstream task in the multivariate time series research field, which plays a crucial role in the public health management process. With the rise of deep learning methods, many studies have focused on the epidemic prediction problem. However, recent primary prediction techniques face two challenges: the overcomplicated model and unsatisfactory interpretability. Therefore, this paper proposes an Interpretable Spatial IDentity (ISID) neural network to predict infectious numbers at the regional weekly level, which employs a light model structure and provides post-hoc explanations. First, this paper streamlines the classical spatio-temporal identity model (STID) and retains the optional spatial identity matrix for learning the contagion relationship between regions. Second, the well-known SHapley Additive explanations (SHAP) method was adopted to interpret how the ISID model predicts with multivariate sliding-window time series input data. The prediction accuracy of ISID is compared with several models in the experimental study, and the results show that the proposed ISID model achieves satisfactory epidemic prediction performance. Furthermore, the SHAP result demonstrates that the ISID pays particular attention to the most proximate and remote data in the input sequence (typically 20 steps long) while paying little attention to the intermediate steps. This study contributes to reliable and interpretable epidemic prediction through a more coherent approach for public health experts.

Summary Computational stratigraphy (CompStrat) is a state-of-the-art Earth-modeling method that captures the key heterogeneities in subsurface reservoirs through modeling of the detailed flow and sediment transportation processes in various depositional environments. The method is fully based on physics and generates high-resolution 3D Earth models that are much more geologically realistic than those generated by traditional Earth-modeling methods. It can accurately predict and preserve those spatially continuous but vertically thin and volumetrically insignificant layers, such as shale layers, thus enabling a much more accurate representation of natural reservoir connectivity. In the past few years, CompStrat has been studied mainly within the Earth science community and has not been broadly applied yet in reservoir simulation research and practices. Our objective is to bridge this gap and allow this frontier technology to offer geologically realistic Earth models for reservoir simulation to better understand how various geological features contribute and control subsurface flow patterns and performance, subsequently leading to better integration among Earth modeling, flow simulation, and more reliable reservoir performance predictions. CompStrat models often have large numbers of cells (hundreds of millions or more). A large proportion of them are related to thin shale layers. These thin cells can often cause convergence difficulties in reservoir simulations. We developed a grid coarsening method to dramatically reduce the cell number and the simulation time with minimal alteration of the overall model connectivity characteristics. The method reduces the cell number by 85% (to 93%) and the simulation time by 94% (to 99%) with limited loss of accuracy for representative examples. Without this method, the simulation may take impractically long time to run for large models with complex multiphase flow dynamics. The successful removal of the computational bottleneck enables the application of this frontier Earth-modeling method in high-fidelity reservoir simulation. It also facilitates a detailed understanding of the connection between geology and flow to offer valuable insight for reservoir modeling, production forecast uncertainty analysis, and history matching. We developed a method to label, evaluate, and rank geological features based on their influence on flow performance, with shale layers being the specific focus. The labeling is performed semiautomatically, and the evaluation and ranking are done efficiently with a reduced-physics solver. The result is statistically consistent across multiple realizations. The fluvial-deltaic CompStrat sector model on which our grid coarsening and shale analysis methods were tested is shared in the Supplementary Materials.

Developing novel, energy-saving, and facile approaches to constructing high-performance aerogels is still chal-lenging. Aerogels are most commonly produced by freeze-drying and supercritical drying that require expensive specialty equipment or ambient drying lengthily of solvent exchange precursors. Here, we report a compensation strategy using acrylamide as an assisting solute to enable the construction of conductive polymer aerogels by simple vacuum drying of frozen solids of aqueous poly(3,4-ethylene dioxythiophene)/poly(styrene sulfonate) (PEDOT/PSS). In this approach, the acrylamide crystal sublimates slowly at an elevated temperature (80 or 110 degrees C) to maintain the porous structure of PEDOT/PSS in the solid-state during water evaporation. By tuning PEDOT/PSS to acrylamide weight ratios and drying temperatures, aerogels were produced with low density (6.3-21.6 mg/cm3), high porosity (>99 %), and low shrinkage (5.3 %). Additionally, acrylamide increases the electrical conductivity of PEDOT/PSS by three orders of magnitude (from 0.01 to 81.1 S/m). Morphology and physical properties are further analyzed to reveal aerogel formation and the conductivity enhancement mech-anism. These aerogels are then applied as water-induced electric generators for green energy harvesting. The current work provides an alternative and simplified approach to rapidly fabricating various nanomaterial-based aerogels, replacing the slower and more expensive freeze-drying and supercritical drying.

Compared with the deposition of thin films on a solid substrate, using a liquid substrate provides an atomic level roughness and extra degree of freedom in manipulating the system conditions such as interfacial tension, which can be preferred for the fabrication of high-performance films. Herein, we develop a liquid-substrate-based spontaneous spreading (LSBS) technology that applies miscible liquids of high surface tension as the substrate and spreads the polymer solution under capillary forces for thin-film processing. We demonstrate the LSBS technology using a conductive polymer, poly (3,4-ethylenedioxythiophene)/poly (styrenesulfonate) (PEDOT/ PSS), and the spread thin films were then transferred to various untreated substrates. LSBS technology uses a miscible liquid substrate for the first time and mediates the spreading, solvent removal, and ion exchange process between the liquid substrate and PEDOT/PSS, increasing the crystallinity of the PEDOT/PSS, thereby significantly improving its electrical conductivity. The LSBS mechanism is investigated through the time-space relationship between spreading and solvent removal, from which we confirm the operating window. Our LSBS technology exhibits a spontaneous, scalable, and versatile process for forming polymer thin films with thicknesses from nano to micron scales on liquid substrates, providing an easy route to control the geometry and presenting universally applicable to different materials.

The success of terrorist attacks reflects the capability of terrorists and the vulnerability of the security defense, explainable prediction of the average attack success rate at the country-annual level is crucial for governments. In this study, terrorist attack data from 146 countries between 2002 to 2020 was obtained from the global terrorism database (GTD), and a two-stage prediction task was conducted. First, multiple machine learning models, including XGBoost and Random Forest (RF), are used to predict the average success rate of terrorist attacks in the next year, considering terrorism root factors and statistical results from the previous year. The results show that the RF model performs the best. Second, the prediction outputs of the RF model are explained using interpretable methods, including Accumulate Local Effect (ALE) and SHapley Additive exPlanation (SHAP), to provide counterterrorism insights applicable to countries around the world.

Continuous, one-dimensional (1D) stretchable conductors have attracted significant attention for the development of wearables and soft-matter electronics. Through the use of advanced spinning, printing, and textile technologies, 1D stretchable conductors in the forms of fibers, wires, and yarns can be designed and engineered to meet the demanding requirements for different wearable applications. Several crucial parameters, such as microarchitecture, conductivity, stretchability, and scalability, play essential roles in designing and developing wearable devices and intelligent textiles. Methodologies and fabrication processes have successfully realized 1D conductors that are highly conductive, strong, lightweight, stretchable, and conformable and can be readily integrated with common fabrics and soft matter. This review summarizes the latest advances in continuous, 1D stretchable conductors and emphasizes recent developments in materials, methodologies, fabrication processes, and strategies geared toward applications in electrical interconnects, mechanical sensors, actuators, and heaters. This review classifies 1D conductors into three categories on the basis of their electrical responses: (1) rigid 1D conductors, (2) piezoresistive 1D conductors, and (3) resistance-stable 1D conductors. This review also evaluates the present challenges in these areas and presents perspectives for improving the performance of stretchable 1D conductors for wearable textile and flexible electronic applications.

Cellular-structured aerogels are usually formed by lyophilization with a slow freeze-drying process. However, there remains a challenge to improve the flexibility of a well-formed aerogel with cellular structures. In this study, cellular structural conductive polymer aerogels were fibrillated with the assistance of the post vapor annealing technique and a functional copolymer, polyethylene-block-poly (ethylene glycol) (PBP). The underlying mechanism lies in the thin walls of the cellular-structured aerogel transformed into fibers during the melting and flowing of PBP parts through ethylene glycol vapor annealing. Moreover, the PBP segments of polyethylene and polyethylene glycol (PEG) act as plasticizers and conductivity enhancers, improving the aerogel's flexibility and electrical conductivity. The fibrillated conductive polymer aerogels were used as sensing elements in strain sensors and electrodes in the triboelectric nanogenerator, demonstrating their potential in soft conductors.(c) 2022 Elsevier Ltd. All rights reserved.

Synthesizing of highly conductive conjugated polymers on target substrates must consider both the reaction regime and adhesion. Vapor phase polymerization (VPP) with a relatively simple procedure and mild reaction temperature (40-50 degrees C) is favorable for polymerization of poly(3,4-ethylenedioxythiophene) (PEDOT) on PET substrates. However, achieving high conductivity and good surface adhesion with target substrates has been challenging. Addressing this, we report that weak alkalinity and enriched amine groups of polyethyleneimine (PEI) were used as a base inhibitor and grafting agent during VPP to improve the PEDOT film's electrical and mechanical properties. The base-inhibited VPP results in a needle-like morphology with enhanced crystallinity in PEDOT, and with this, the conductivity of 1980 S/cm has been achieved. Moreover, many amine groups with high chemical activity in PEI make it successfully grafted on the polydopamine (PDA)-coated substrates. The surface grafting of PEI in PEDOT with PDA through Schiff base and Michael addition reactions make the PEDOT film resistant to ultrasonic cleaning in water or organic electrolyte, presenting significant potential in applying washable and cleanable electronics. We then demonstrate an anti-ultrasonic cleaning electrochromic display based on vapor phase polymerized PEDOT. This work experimentally confirms that PEI is critical in ensuring high-performance PEDOT films. Incorporating this technique on a PET-based medical face shield renders robust wearable electronics.

Composite porous supercapacitor electrodes were prepared by growing poly(3,4-ethylenedioxythiophene) (PEDOT) on graphite nanoplatelet- or graphene nanoplatelet-deposited open-cell polyurethane (PU) sponges via a vapor phase polymerization (VPP) method. The resulting composite supercapacitor electrodes exhibited great capacitive performance, with PEDOT acting as both the conductive binder and the active material. The chemical composition was characterized by Raman spectroscopy and the surface morphology was characterized by scanning electron microscopy (SEM). Cyclic voltammetry (CV), charge-discharge (CD) tests and electrochemical impedance spectroscopy were utilized to study the electrical performance of the composite electrodes produced in symmetrically configured supercapacitor cells. The carbon material deposited on PU substrates and the polymerization temperature of PEDOT affected significantly the PEDOT morphology and the electrical properties of the resulting composite sponges. The highest areal specific capacitance 798.2 mF cm−2 was obtained with the composite sponge fabricated by VPP of PEDOT at 110 °C with graphene nanoplatelet-deposited PU sponge substrate. The capacitance retention of this composite electrode was 101.0% after 10,000 charging–discharging cycles. The high flexibility, high areal specific capacitance, excellent long-term cycling stability and low cost make these composite sponges promising electrode materials for supercapacitors.

Decline curve analysis (DCA) has been widely applied in production forecasting of wells in unconventional hydrocarbon reservoirs. However, traditional curve-fit-based methods fall short of forecast accuracy due to three weaknesses: first, they cannot capture the reservoir signals not modeled by the underlying DCA model formulas; second, when predicting the production of a target well, the production history of other wells in the geologic formation (which is valuable information) is not considered; third, the wells' geographic, geologic, wellbore, well spacing, and completion properties, which are highly relevant to production capability, are not used. More recent approaches have begun replacing traditional DCA with machine-learning methods [e.g., random forest (RF), support vector regression (SVR), etc.] for production forecast. Nevertheless, these methods are still suboptimal in detecting similar production trends in different wells, leading to large forecast error. A new and simple method called dynamic production rescaling (DPR) is developed to improve the accuracy of machine-learning DCA (ML-DCA). By combining DPR with common ML-DCA methods, we observe that the error mean, deviation, and skewness can be significantly reduced by 15 to 35% compared with ML-DCA without DPR. The error reduction is 30 to 60% compared with automatic curve fit of the traditional modified Arps DCA model. DPR has been tested successfully on monthly production data of over 20,000 unconventional horizontal wells in the Permian and Appalachian basins for both long- and short-term forecasts. The significant error reduction is consistent across different basins and formations. DPR is computationally efficient, so a large number of wells can be analyzed automatically and quickly. Moreover, the effectiveness and efficiency of DPR is independent of the underlying machine-learning algorithm, further demonstrating its robustness.

Design of Experiments (DoE) is one of the most commonly employed techniques in the petroleum industry for Assisted History Matching (AHM) and uncertainty analysis of reservoir production forecasts. Although conceptually straightforward, DoE is often misused by practitioners because many of its statistical and modeling principles are not carefully followed. Our earlier paper (Li et al. 2019) detailed the best practices in DoE-based AHM for brownfields. However, to our best knowledge, there is a lack of studies that summarize the common caveats and pitfalls in DoE-based production forecast uncertainty analysis for greenfields and history-matched brownfields. Our objective here is to summarize these caveats and pitfalls to help practitioners apply the correct principles for DoE-based production forecast uncertainty analysis. Over 60 common pitfalls in all stages of a DoE workflow are summarized. Special attention is paid to the following critical project transitions: (1) the transition from static earth modeling to dynamic reservoir simulation; (2) from AHM to production forecast; and (3) from analyzing subsurface uncertainties to analyzing field-development alternatives. Most pitfalls can be avoided by consistently following the statistical and modeling principles. Some pitfalls, however, can trap experienced engineers. For example, mistakes made in handling the three abovementioned transitions can yield strongly unreliable proxy and sensitivity analysis. For the representative examples we study, they can lead to having a proxy R2 of less than 0.2 versus larger than 0.9 if done correctly. Two improved experimental designs are created to resolve this challenge. Besides the technical pitfalls that are avoidable via robust statistical workflows, we also highlight the often more severe non-technical pitfalls that cannot be evaluated by measures like R2. Thoughts are shared on how they can be avoided, especially during project framing and the three critical transition scenarios.

A flexible solid-state supercapacitor based on vapor phase polymerized (VPP) PEDOT into cellulose paper matrix (PEDOT/CP) was successfully fabricated. The PEDOT/CP composite material worked as both current collector and electrode in constructed test cells. It had a low sheet resistance of 14 Omega/square and survived the Scotch tape test for adhesion. It also showed excellent stability with no significant conductivity drop after 1000 cycles of bending. The PEDOT from electrode obtained the mass specific capacitance of 179 F/g at scan rate of 10 mV/s, which was among the highest specific capacitances ever reported. This high capacitance was attributed to the combination of the VPP technique and the porous fibrous structure of the cellulose matrix. The EDOT vapor penetrated and polymerized through the CP matrix made of nanometer to micrometer level CP fibers. The highest electrode volumetric capacitance achieved was 13.7 F/cm(3). The whole device achieved an energy density of 0.76 mWh/cm(3) and a power density of 0.01 W/cm(3). Bending the supercapacitor to 90 degrees or rotating to 45 degrees caused no major change in capacitance. Owing to the all nonmetallic materials used to construct the supercapacitor, it can be easily disposed. The incineration of the supercapacitor does not release significant hazardous exhaust.

Optically transparent and highly conductive poly(3,4-ethylenedioxythiophene) (PEDOT) thin films were grown through vapor phase polymerization on (3-Mercaptopropyl)trimethoxysilane functionalized 3,4-Ethylenedioxythiophene (MPTMS functionalized EDOT) grafted glass substrates. Compared to bare glass, the EDOT grafted surface led to enhancements in both electrical conductivity and adhesion of PEDOT thin films. A quinoid-rich structure with increased crystallinity and a further enhanced conductivity was induced by post-deposition sulfuric acid doping. X-ray diffraction showed different orientations of the PEDOT crystals grown on substrates with and without EDOT grafting. The highest conductivity of 2690 S/cm, with an average optical transmittance of 95.4 % in the visible range, was achieved when PEDOT was vapor phase polymerized on EDOT grafted substrates and doped with 98 % sulfuric acid. Photostability was tested using a xenon arc light source and characterized by attenuated total reflection Fourier-transform infrared spectroscopy, showing that photoinduced degradation is associated with a decrease in C=C double bond content.

Fluorine doped metal oxides have shown great promise for use in many applications including Li-ion batteries (LiBs), photocatalysis and dye-sensitized solar cells. Both F-TiO2 and TiOF2 have been studied extensively for this purpose. However, fabrication of fluorine doped titanium oxide requires the use of dangerous fluorinated chemicals such as trifluoroacetic acid, hydrofluoric acid and fluorine gas which are both difficult to handle and create significant waste detrimental for the environment. Additionally, current procedures for fabrication of FTiO2 and TiOF2 require long heating times which are inefficient, wasting large amounts of energy. Because of these factors, fabrication of F-TiO2, TiOF2 and of other important fluorine doped metal oxides is expensive and dangerous to make. In this work, two new methods were used to greatly improve the safety and efficiency of synthesis. First, the fluorinated waste was eliminated by using the safe and inert polymer, polyvinylidene fluoride (PVDF), as the fluorine source. Second, microwave (MW) irradiation was used to reduce the time and energy required for synthesis by addition of the MW absorber, graphene, into the precursor material. The results show that when using the PVDF fluorine source with conventional oven heating it results in low levels of F-TiO2 which are dependent on heating temperatures. However, when using microwave irradiation high levels of doping were achieved creating both F-TiO2 and TiOF2 in as little as 6 min compared to several hours. This work has shown that by combining both PVDF and microwave irradiation fabrication of F-TiO2 and TiOF2 can now be done safely and efficiently with greatly reduced environmental impact.