Exploring the methods on improving CH4 delivery performance to surpass the Advanced Research Project Ageney-Energy target

CH_4 storage associated with adsorbed natural gas(ANG) technology is an issue attracting great concern.Following the Advanced Research Project Agency-Energy(ARPA-E) targeted deliverable capacity of 315 cm~3·cm~(-3)(STP), hundreds of thousands of materials have been experimentally or theoretically evaluated,while the best results still show a 35% gap from the target. Moreover, recent theoretical research reveals that the target is beyond the possibility that real materials can be designed. To get rid of the awkward situation, we make attempts on investigating the CH_4 delivery performance under other operation conditions. Methods of raising the discharge temperature(to infinite high) or elevating the storage pressure(to 25 MPa) have been proved to show limited effectiveness. In this work, it is found that the ARPA-E target can be achieved by using a decreasing storage temperature strategy. By taking 280 Co RE(computation-ready, experimental) COFs(covalent organic frameworks) as ANG materials, when reduce the storage temperature to 190.6 K, the highest deliverable capacity can reach 392 cm~3·cm~(-3)(STP), and16.1% Co RE COFs can surpass the target. The target is also achievable when storage at 220 K. Structure performance relationships study shows strong correlation between deliverable capacity and void fraction. Hence, 120 hypothetical COFs are generated to ascertain the optimum void fraction. In addition,the performance of 2 D-COFs can be greatly enhanced by increasing the interlayer spacings, e.g. CH_4 deliverable capacity(storage at 190.6 K) of ATFG-COF can be improved from 239 to 411 cm~3·cm~(-3)(STP) when interlayer spacing is enlarged to 1.65 nm.     

Active learning boosted computational discovery of covalent–organic frameworks for ultrahigh CH4 storage

As an environmental-benign fuel, methane (CH₄) has received considerable interest for developing high-capacity energy storage systems. Herein, we aim to rapidly discover covalent–organic frameworks (COFs) for ultrahigh CH₄ storage among 530,000+ COFs, including one experimental (Curated) and two hypothetical (Berkeley and Genomic) databases. First, the feature space of all the three COF databases is projected by t-Distributed Stochastic Neighbor Embedding (t-SNE) technique, which reveals a potential but unexplored regime in Genomic COFs. Subsequently, an active learning (AL) approach is developed by integrating parallel acquisition with molecular simulation to efficiently explore Genomic COFs. The parallel AL model demonstrates remarkable screening efficiency and shortlists top COFs by evaluating only 50 out of 445,845 Genomic COFs. A record-breaking Genomic COF is identified with CH₄ deliverable capacity of 222.2 v/v, surpassing the current world record (208.0 v/v from experiment and 217.9 v/v from simulation). Our AL approach is significantly faster than brute-force simulation and conventional machine learning, it would accelerate the discovery of advanced porous materials for broad applications.     

Prediction of asphaltene adsorption capacity of clay minerals using machine learning

A thorough understanding of asphaltene adsorption on clay minerals is particularly important in oil production and contaminated soil remediation using clay-based adsorbents. In this paper, we introduced a machine learning approach as a reliable alternative for commonly used adsorption isotherms that suffer from inherent limitations in the prediction of asphaltene adsorption onto clay minerals. Machine learning (ML) models, namely multilayer perceptron (MLP), support vector machine (SVM), decision tree (DT), random forest (RF), and committee machine intelligent system (CMIS) combined with two optimizers were used. Experimental data (142 data points for six different clay minerals) was used for the modelling. To improve the accuracy of the smart models, a comprehensive data preparation such as outlier removal and feature selection was carried out. The results showed that relatively all the proposed models predict asphaltene adsorption on clay minerals with acceptable precision. Nevertheless, the MLP model showed superior performance compared with other models in which the overall root mean square error (RMSE) and coefficient of determination (R²) values of 6.72 and 0.93 were obtained, respectively. Finally, the developed MLP model was compared with the well-known adsorption isotherms of Langmuir and Freundlich and exhibited superior performance.     

Methane storage capacities and pore textures of active carbons undergoing mechanical densification

Chemically activated anthracites with pore textures optimised for methane storage were densified by mixing suitable granular fractions, and the prepared mixtures were submitted to increasingly high mechanical pressures (up to 220 kg cm⁻²). The effect of densification was investigated and discussed. It was shown that, while the apparent densities increase significantly, compressing of the adsorbent has no effect on the adsorption capacities. This was confirmed by the measurement of surface areas and micropore volumes using several probe molecules; the microporosity of the material undergoing compression was found to be generally unchanged, whatever the applied pressure and whatever the burn-off. Densification of the material decreases the amount of compressed gas within the storage vessel and, as a result, the global mass storage capacities were found to decrease with the compacting force. Consequently, it was shown that because the density and mass capacities vary in opposite directions, the volume storage capacity should not be extrapolated from measurements made on uncompacted material. An optimal compaction pressure close to 100 kg cm⁻², corresponding to maxima of volume storage capacities of methane, was evidenced and found as 193 V/V stored and 163 V/V deliverable at 3.5 MPa and 20 °C.     

连锁结构金属-有机骨架材料强化甲烷吸附的分子模拟研究

A systematic molecular simulation study was performed to investigate the effect of catenation on methane adsorption in metal-organic frameworks (MOFs). Four pairs of isoreticular MOFs (IRMOFs) with and without catenation were adopted and their capacities for methane adsorption were compared at room temperature. The pre-sent work showed that catenation could greatly enhance the storage capacity of methane in MOFs, due to the for-mation of additional small pores and adsorption sites formed by the catenation of frameworks. In addition, the simulation results obtained at 298 K and 3.5 MPa showed that catenated MOFs could easily meet the requirement for methane storage in porous materials.     

Multi-scale theoretical investigation of hydrogen storage in covalent organic frameworks

The quest for efficient hydrogen storage materials has been the limiting step towards the commercialization of hydrogen as an energy carrier and has attracted a lot of attention from the scientific community. Sophisticated multi-scale theoretical techniques have been considered as a valuable tool for the prediction of materials storage properties. Such techniques have also been used for the investigation of hydrogen storage in a novel category of porous materials known as Covalent Organic Frameworks (COFs). These framework materials are consisted of light elements and are characterized by exceptional physicochemical properties such as large surface areas and pore volumes. Combinations of ab initio, Molecular Dynamics (MD) and Grand Canonical Monte-Carlo (GCMC) calculations have been performed to investigate the hydrogen adsorption in these ultra-light materials. The purpose of the present review is to summarize the theoretical hydrogen storage studies that have been published after the discovery of COFs. Experimental and theoretical studies have proven that COFs have comparable or better hydrogen storage abilities than other competitive materials such as MOF. The key factors that can lead to the improvement of the hydrogen storage properties of COFs are highlighted, accompanied with some recently presented theoretical multi-scale studies concerning these factors.     

Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to the negative effect on chemical durability often associated with its formation. Developing models to accurately predict nepheline precipitation from compositions is important for increasing waste loading since existing models can be overly conservative. In this study, an expanded dataset of 955 glasses, including 352 high-level waste glasses, was compiled from literature data. Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. In addition, nine machine learning (ML) algorithms (k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability to predict nepheline precipitation from glass compositions. Model accuracy, precision, recall/sensitivity, and F1 scores were systemically compared between different ML algorithms and modeling protocols. Model prediction with an accuracy of ~0.9 (misclassification rate of ~10%) was observed for different algorithms under certain protocols. This study evaluated various ML models to predict nepheline precipitation in waste glasses, highlighting the importance of data preparation and modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitation.     

The effect of wetting on pore texture and methane storage ability of NaOH activated anthracite

The present work deals with the role of water commonly present in carbonaceous materials, or added to them, on the NaOH activation process. The preliminary wetting of an anthracite subsequently activated with NaOH in definite conditions (1 h at 730 °C, mass ratio NaOH/carbon = 3) and its consequence on the pore texture and resulting methane adsorption capacities are discussed. Water was added to a powder of anthracite according to wetting ratios water/carbon ranging from 0% to 30%. Significant effects on BET surface area, pore texture, packing density and corresponding methane storage capacity were evidenced. Among the investigated wetting ratios, an optimum of 20–25 wt.% of water added to the anthracite was found to lead to the highest adsorption properties for methane. An additional densification finally allowed reaching deliverable methane capacities higher than 150 V/V.     

Random forest models to predict the densities and surface tensions of deep eutectic solvents

The use of machine learning in physicochemical properties modeling has great potential to accelerate the application of emerging materials. Deep eutectic solvents (DESs), an emerging class of solvents, are promising for applications as inexpensive “designer” solvents. Due to the unique structure of DESs, the hydrogen bond donor and hydrogen bond acceptor can be varied to create a mixture with specific physical properties. In this work, we proposed random forest (RF) models to predict the densities and the surface tensions of DESs, which are essential for the separation process. In the proposed models, the structural information and the calculated critical properties were used as two different types of features, respectively. The results demonstrate that the RF models predict the densities and surface tensions of DESs with high accuracy, with absolute average relative deviation (AARD%) less than 1% in the prediction of density and 3% in the prediction of surface tension.     

Flow regime identification in aerated stirred vessel using passive acoustic emission and machine learning

Smart at- or online process sensors, which employ machine learning (ML) to process data, have been the subject of extensive research in recent years, due to their potential for real-time process control. In this paper, a passive acoustic emission process sensor has been used to detect gas–liquid regimes within a stirred, aerated vessel using novel ML approaches. Pressure fluctuations (acoustic emissions) in an air-water system were recorded using a piezoelectric sensor installed on the external wall of three identical cylindrical tanks of diameter, T = 160 mm, filled to a volume of 5 L (height, H = 1.5 T). The tanks were made of either glass, steel, or aluminium, and each tank was equipped with a Rushton turbine of diameter, D = 0.35 T. The investigated flow regimes, flooding, loading, complete dispersion, and un-gassed, were obtained by changing the air feed flow rates and by varying the impeller speed. The acoustic spectra obtained were processed to select an optimal number of features characterizing each of the regimes, and these were used to train three different ML algorithms. The pre-processing includes a principal component analysis (PCA) step, which reduces the volume of data fed to the ML algorithms, saving computational time up to a factor of 5. The algorithms (decision tree, k-nearest neighbour, and support vector machines) were challenged to use these features to identify the correct flow regime. Accurate predictions of the three gas–liquid regimes of interest have been achieved. The accuracy of the prediction ranges from 90% to 99%, and this difference is related to the material used for the vessel.     

共价有机框架材料的最新进展

共价有机框架材料（covalent organic frameworks，COFs）是一类新兴的材料，是一种由有机构筑基元构成并用可逆的共价键进行连接、具有结晶性和周期性的多孔材料。因为这种材料比表面积大、密度低，拥有多样性的结构、优秀的热稳定性及孔道易修饰等优点，越来越受到人们的关注。本文综述了近年来COFs的最新发展动态，将其按照基底材料不同进行分类，介绍COFs在储能、光电、催化、生物医药等方面的应用和发展，包括气体的吸附和存储、材料的光电导性、催化反应进行的性能、手性分离和药物缓控释等；讨论了COFs结构的表征以及相比于其他材料所具有的优越特性。最后指出COFs未来发展趋势是合成具有高度稳定性、结构可控、成本低廉的功能性材料，并对其在实际中的应用前景进行展望。     

Progress of superhydrophobic porous materials

Porous materials such as metal organic framework materials (MOFs), covalent organic framework materials (COFs), organic porous polymers (POPs), etc., have been used widely used in the fields of separation, catalysis, gas storage and drug release due to their diversity, designability, controllability and functionalization of pores. Despite these promising applications, some of the porous materials suffer from moisture-sensitivity and instability in aqueous media due to their inherent structural features. To overcome this problem, endowing them with hydrophobicity is an effective strategy. However, designing superhydrophobic porous materials has certain challenges. In this work, the progress of MOFs, COFs and POPs with (super-)hydrophobic property is introduced. Issues related to their design strategy, structures, and practical applications such as catalysis, oil/water separation and gas storage and separation were analyzed. Additionally, the current problems and the future research directions of the hydrophobic porous materials were discussed.     

超疏水多孔材料的研究进展

多孔材料如金属有机框架材料（MOFs）、共价有机框架材料（COFs）、有机多孔聚合物（POPs）等由于构筑单元的多样性、可设计性,孔道的可调控性和功能化,已经被广泛用于分离、催化、气体储存以及药物释放等领域。尽管如此,这些多孔材料固有的结构特征让它们普遍对水气非常敏感,最严重时多孔结构在水溶液环境下会坍塌。为解决此类问题,制备疏水的多孔材料是一个非常好的策略。然而,设计超疏水多孔材料具有一定的挑战。介绍了具有（超）疏水性能的MOFs、COFs和POPs的发展现状,对超疏水多孔材料合成思路和结构特点进行了分析,对这类材料在催化、油水分离、气体吸附和分离等方面的应用进行了总结,并进一步探讨了此类材料存在的问题和发展方向。     

Adsorption of methane on porous metal carboxylates

Porous metal carboxylates such as MIL-101 (Cr-terephthalate), MIL-100-Cr (Cr-trimesate) and MIL-100-Fe (Fe-trimesate) with very high porosity have been tested as potential adsorbents for methane storage materials. The MIL-101 shows one of the highest adsorption capacities for methane. The adsorption capacity per weight increases with increasing BET surface area or micropore volume irrespective of the structure, type of metal ions such as Cr³⁺ and Fe³⁺. This result suggests that the porous adsorbent for methane should have high porosity rather than special adsorption sites or structures.     

基于FUSION模型的碳基固废热解产物产率预测

低温热解是清洁转化碳基固废、实现汇碳和减排的成熟有效方法之一。通过建立预测碳基固废热解产物产率的数学模型可以极大缩短科研探索时间,优化调控热解反应过程。本研究以80组热解实验数据为样本,首先对神经网络（ML）、支持向量机（SVM）和线性回归（LR）模型进行训练和测试,分析机器学习的有效性,然后将三种模型通过算法融合,建立具有自适应性的FUSION模型。最后,利用实验数据对该模型进行进一步的训练和测试,形成适合预测碳基固废热解产物的数据模型。融合模型能够有效解决单一模型在预测碳基固废热解产物分布过程中,受热解交互作用影响,预测精度波动的问题。同时,该模型预测值精度较高,预测值与实验值的相对误差<2%。     

Preparation of powdered activated carbon from rice husk and its methane adsorption properties

Success of adsorbed natural gas (ANG) storage process is mainly based on the characteristics of the adsorbent, so various synthesized adsorbents were analyzed for methane adsorption on a thermodynamic basis. Activated carbon from rice husk (AC-RH) was synthesized and its methane adsorption capacities were compared with phenol based activated carbons (AC-PH₂O and AC-PKOH). The adsorption experiments were conducted by volumetric method under various constant temperatures (293.15, 303.15, 313.15 and 323.15 K) and pressure up to 3.5MPa. Maximum methane adsorption was observed in AC-RH as its surface area is higher than the other two adsorbents. The experimental data were correlated well with Langmuir-Fruendlich isotherms. In addition, isosteric heat of adsorption was calculated by using Clausius-Clapeyron equation.     

Temperature Sensitive Nanocapsule of Complex Structural Form for Methane Storage

The processes of methane adsorption, storage and desorption by the nanocapsule are investigated with molecular-dynamic modeling method. The specific nanocapsule shape defines its functioning uniqueness: methane is adsorbed under 40 MPa and at normal temperature with further blocking of methane molecules the K@C60¹⁺ endohedral complex in the nanocapsule by external electric field, the storage is performed under normal external conditions, and methane desorption is performed at 350 K. The methane content in the nanocapsule during storage reaches 11.09 mass%. The nanocapsule consists of tree parts: storage chamber, junction and blocking chamber. The storage chamber comprises the nanotube (20,20). The blocking chamber is a short nanotube (20,20) with three holes. The junction consists of the nanotube (10,10) and nanotube (8,8); moreover, the nanotube (8,8) is connected with the storage chamber and nanotube (10,10) with the blocking chamber. The blocking chamber is opened and closed by the transfer of the K@C₆₀ ¹⁺ endohedral complex under electrostatic field action.     

NaOH activation of anthracites: effect of temperature on pore textures and methane storage ability

The main purpose of this work was to prepare various active carbons from the same precursor at various activation temperatures, and investigate both porosity development and corresponding methane storage capacities. An anthracite was thus chemically activated with sodium hydroxide under nitrogen flow at temperatures ranging from 600 to 830 °C, with a constant mass ratio: hydroxide/anthracite = 3. The pore textures of the corresponding activated carbons were investigated using the adsorption isotherms of four probe molecules characterised by their increasing molecular diameters, namely CO₂, N₂, C₆H₆ and CCl₄. The changes occurring in each kind of pores were discussed and put in relation with the activation temperature. The specific volumes of different micro- and mesopore families were measured and discussed. Depending on the temperature range, two different activation mechanisms were evidenced. Methane storage isotherms at 20 °C and up to 3.5 MPa were measured for the investigated materials. Linear correlations between various textural parameters and methane storage capacities were given. Additionally, a number of results previously reported in the literature were confirmed by the present work.