High Pressure Temperature Conditions Research Articles

Effect of temperature on transport index of fish oil-based drilling mud in high pressure high temperature well: a hybrid model approach

Diesel Oil-based drilling mud used in the oil and gas industry causes severe environmental and public health issues due to its high toxicity, especially in high-pressure, high-temperature (HPHT) wells. Therefore, easily biodegradable synthetic oil-based mud with high drilling performance is essential. Fish oil is a bio-oil known for its non-toxicity, high lubricity, thermal stability, and biodegradability. Effective hole cleaning is crucial for drilling performance and is ensured by the rheological properties of drilling mud. While rheological parameter prediction can be performed using an artificial neural network (ANN) based model, such models often fail to provide accurate predictions for unseen data. In contrast, empirical models are more robust and facilitate generalization. This study aims at assessment of the suitability of invert emulsion fish oil-based drilling mud (IEFOBDM) in HPHT wells through evaluation of hole cleaning performance (HCP) using a hybrid approach that combines an ANN with nonlinear regression (NLR) for temperatures ranging from 40 °C to 80 °C.Firstly, an ANN model was developed considering mud weight, temperature, and shear rate. Secondly, the NLR model was used in the prediction of rheological parameters, namely, Yield point (YP) and Plastic viscosity (PV), based on the predicted shear stress from the ANN model. Finally, the performance metrics of the ANN-NLR rheological model were evaluated using the root mean square error (RMSE) and correlation coefficient (R2) and comparison made with the Single ANN rheological model. The results showed the outperformance of ANN-NLR model over the ANN model for rheological parameter prediction, with R2 values of (0.99) for YP and (1) for PV. The predicted PV and YP values were then used for evaluation of transport index (TI) for HCP analysis. The effect of temperature on TI analysis showed this with increase in temperature, TI also increased, indicating the proposed mud providing good HCP under HPHT conditions.

Innovative machine learning for drilling fluid density prediction: a novel central force search-adaptive XGBoost in HPHT environments

Oil and gas industries are facing a special dilemma when it comes to high-pressure, high-temperature (HPHT) drilling as the accurate forecasting of the drilling fluid density (DFD) is a vital factor for safe and efficient operations. Complicated relationships and inconsistencies in HPHT situations are rarely mapped by current forecasting models, while their buggy performance and safety risks during drilling can be underestimated. In this research, we propose a novel machine learning (ML) approach to enhance the accuracy of DFD anticipation under HPHT conditions: central force search-adaptive extreme gradient boosting (CFS-XGB). This paper uses a dataset that has drilling variables together with the DFD for HPHT situations to examine the accuracy of the CFS-XGB model. Excluding the abnormalities of data or mistakes, the reliability of the original data is maintained by applying min–max normalization. After that, finding the important features with the help of the boosted principal component analysis (BPCA) approach to the normalized data will ensure a major improvement in the CFS-XGB methodology’s prediction efficacy. This research is experimented in the Python platform, and the performance of the proposed CFS-XGB method is analyzed in terms of MSE, R2, and AAPRE metrics. The suggested approach performs better than the current methods in forecasting the drilling fluid concentration in HPHT settings, according to the experimental data. This development in predictive modeling helps increase the productivity and safety of drilling operations, which will eventually help the oil and gas sector manage the challenges posed by HPHT drilling settings.

Modeling drilling fluid density at high-pressure high-temperature conditions using advanced machine-learning techniques

The management of oil and gas wells under high-pressure high-temperature (HPHT) conditions is a significant challenge in the oil industry, leading to increased operational and maintenance costs. One method for calculating the density of drilling fluids involves the use of sensors placed at the bottom of the well, which measure the fluid density in real time. Despite their accuracy, these sensors are costly and perform suboptimal under HPHT conditions. In the present study, seven robust machine-learning models, namely generalized regression neural networks (GRNN), wavelet neural networks (WNN), and cascade forward neural networks (CFNN) combined with two different training algorithms containing Levenberg-Marquardt and Bayesian regression (CFNN-LM and CFNN-BR), respectively, and support vector regression (SVR) models combined with three optimization algorithms containing (farmland fertility algorithm (FFA) grasshopper optimization algorithm (GOA), and particle swarm optimization (PSO) were utilized to predict mud density at HPHT conditions. Moreover, mathematical models were developed using a group data management algorithm (GMDH) to predict this parameter. WNN, SVR model combined with (PSO, GOA, FFA), CFNN, and GRNN algorithms have better adaptability than other artificial intelligence models. These models can improve performance by changing the data and different situations. Also, these models can better resistance against noisy data, which means that they can show good performance compared to uneven and discontinuous data. To this aim, using 986 experimental data of drilling fluid at HPHT, including 117 water-base fluid data, 440 colloidal gas aphron fluid data, 219 oil-base fluid data, and 210 synthetic fluid data the drilling fluid density was modeled. Input parameters including temperature, pressure, initial mud density, and volume fraction of solid content were used for modeling. The results showed that the volume fraction of the solid content as an input variable improves the accuracy of intelligent models in this work. Based on average absolute percent relative error (AAPRE) the best outcomes for predicting the mud density without the input parameter of solid fraction were observed by the PSO-SVR model with an AAPRE of 0.5569%. Following, the GOA-SVR, WNN, and FFA-SVR delivered the next best results with AAPRE of 0.5860%, 0.5786%, and 0.6148%, respectively. Moreover, with the input parameter of solid fraction, the WNN model demonstrated the highest accuracy among all models, achieving an AAPRE of 0.2475%. Also, the correlation developed using the GMDH method based on input parameters of initial mud density, temperature, and pressure yielded satisfactory outcomes and can provide precise and swift estimates. Eventually, Sensitivity analysis revealed that initial mud density had the highest impact on mud density. Lastly, the leverage analysis indicates that most data points are trustworthy based on experimentation, with only a few falling outside the PSO-SVR and WNN models' scope.

Molecular geometry specific Monte Carlo simulation of the efficacy of diamond crystal formation from diamondoids

Diamondoids are a class of organic molecules with the carbon skeletons isostructural to nano-diamond, and have been shown to be promising precursors for diamond formation. In this work, the formation of diamond crystals from various diamondoid molecule building blocks was studied using our developed molecular geometry specific Monte Carlo method. We maintained the internal carbon skeletons of the diamondoid molecules, and investigated how the carbon-carbon bonds form between diamondoid molecules and how efficient the process is to form diamond crystals. The simulations show that higher diamondoid molecules can produce structures closer to a diamond crystal compared with lower diamondoid molecules. Specifically, using higher diamondoid molecules, larger bulk diamond crystals are formed with fewer vacancies. The higher propensity of certain diamondoids to form diamond crystals reveals insights into the microscopic processes of diamond formation under high-pressure high-temperature conditions.

Modeling interfacial tension of methane-brine systems at high pressure high temperature conditions

In the present study, two white-box correlative models, namely gene expression programming (GEP) and group method of data handling (GMDH), were implemented to predict the interfacial tension (IFT) of brine-methane system. To achieve this, 790 data points were collected from literature, with pressure, temperature, and salinity as inputs and IFT as the output. The range of salinity, pressure, and temperature is from 0 to 107000 ppm, 0.01–260 MPa, and 278.1–477.59 K, respectively. The results show that the GMDH model with an R2 of 0.9321 and an average absolute percent relative error (AAPRE) of 3.68% outperforms the GEP, which has an AAPRE of 5.08% and an R2 of 0.8943. Based on our investigation, GMDH and GEP models follow the expected trend of IFT with the variation of pressure. It has been found that the GMDH model exhibits better performance compared to the previously developed correlations in the literature. Furthermore, the sensitivity analysis indicated that temperature has the greatest effect on IFT. By using the leverage approach, we proved that the GMDH model is statistically valid.

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions.

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. This study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure-temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.

Weyl semimetallic phase in high pressure CrSb2 and structural compression studies of its high pressure polymorphs

In this study, high pressure synchrotron powder X-ray diffraction is used to investigate the compression of two high pressure polymorphs of CrSb2. The first is the CuAl2-type polymorph with an eight-fold coordinated Cr, which can be quenched to ambient conditions from high-pressure high-temperature conditions. The second is the recently discovered MoP2-type polymorph, which is induced by compression at room temperature, with a seven-fold coordinated Cr. Here, the assigned structure is unambiguously confirmed by solving it from single-crystal X-ray diffraction. Furthermore, the electrical properties of the MoP2-type polymorph were investigated theoretically and the resistance calculations under pressure were accompanied by resistance measurements under high pressure on a single crystal of CrSb2. The calculated electronic band structure for the MoP2-type phase is discussed and we show that the polymorph is semimetallic and possesses type-I Weyl points. No further phase transitions were observed for the CuAl2-type structure up to 50 GPa and 40 GPa for the MoP2-type structure. Even though the CuAl2-phase has the highest coordination number of Cr, it was found to be less compressible than the MoP2-phase having a seven-fold coordinated Cr, which was attributed to the longer Cr-Sb distance in the CuAl2-type phase. The discovery of a type-I Weyl semimetallic phase in CrSb2 opens up for discovering other Weyl semimetals in the transition metal di-pnictides under high pressure.

Anisotropic thermal conductivity of antigorite along slab subduction impacts seismicity of intermediate-depth earthquakes

Double seismic zones (DSZs) are a feature of some subducting slabs, where intermediate-depth earthquakes (~70–300 km) align along two separate planes. The upper seismic plane is generally attributed to dehydration embrittlement, whereas mechanisms forming the lower seismic plane are still debated. Thermal conductivity of slab minerals is expected to control the temperature evolution of subducting slabs, and therefore their seismicity. However, effects of the potential anisotropic thermal conductivity of layered serpentine minerals with crystal preferred orientation on slab’s thermal evolution remain poorly understood. Here we measure the lattice thermal conductivity of antigorite, a hydrous serpentine mineral, along its crystallographic b- and c-axis at relevant high pressure-temperature conditions of subduction. We find that antigorite’s thermal conductivity along the c-axis is ~3–4 folds smaller than the b-axis. Our numerical models further reveal that when the low-thermal-conductivity c-axis is aligned normal to the slab dip, antigorite’s strongly anisotropic thermal conductivity enables heating at the top portion of the slab, facilitating dehydration embrittlement that causes the seismicity in the upper plane of DSZs. Potentially, the antigorite’s thermal insulating effect also hinders the dissipation of frictional heat inside shear zones, promoting thermal runaway along serpentinized faults that could trigger intermediate-depth earthquakes.

In Situ Measurement Techniques Using Diamond Anvil Cell at High Pressure–Temperature Conditions: A Review

Diamond anvil cells have garnered significant attention in high‐pressure studies as a valuable tool for investigating material preparation, phase transition dynamics, and ultra‐high‐pressure physical chemistry. Its potential applications span fields such as materials science, condensed matter physics, chemistry, and geology. This study conducts a comprehensive review of the utilization of laser‐heated diamond anvil cell (LH‐DAC) devices in conjunction with in situ optical characterization techniques, such as X‐Ray and Raman scattering. Further, diverse in situ performance measurement methods encompassing electrical, thermal, magnetic, and acoustic analyses are examined. The development and role of the prevailing in situ measurement techniques have been described, along with the current progress in applied research for each technique. This study aims to facilitate the discovery of new structures and properties of materials under high pressure–temperature conditions.

Performance Assessment of Bismuth Ferrite Nanoparticles in Enhancing Oilwell Cement Properties: Implications for Sustainable Construction

Summary Oilwell cement ensures wellbore stability and isolates zones while bearing casing load and formation pressure. Its properties, crucial in extreme downhole conditions, include compressive strength, fluid loss resistance, and durability. In the present work, bismuth ferrite nanoparticles (BFO NPs) were synthesized using the sol-gel method and used as an additive in oilwell cement. The synthesized BFO NPs were characterized using Fourier transform infrared (FTIR), X-ray diffraction (XRD), field-emission scanning electron microscope (FESEM), and dynamic light scattering (DLS) techniques to analyze the functional groups, crystalline structure, morphological features, and hydrodynamic size distribution. Tests at 70°C and 2,000 psi revealed that 1% by weight of cement (BWOC) BFO NPs increased compressive strength by ~136% and reduced fluid loss to ~64% compared with base cement. It can be conjectured that the exposed facets of BFO NPs containing oxygen act as nucleating sites that promote the ordering of the silicate tetrahedra, thereby increasing the strength and crystallinity and reducing the water loss. The experimental results confirm that the BFO NPs can improve the properties of oilwell cement slurry at high-pressure, high-temperature (HPHT) conditions. This research underscores the potential of BFO NPs as sustainable additives for optimizing oilwell cement performance under challenging HPHT conditions, paving the way for advancements in sustainable construction practices.

Annealing process and temperature effects on silicon-vacancy and germanium-vacancy centers in CVD grown polycrystalline diamond

The annealing treatment plays a crucial role in tailoring the properties of synthetic diamond materials, especially those doped with various elements in order to form specific color centers like nitrogen-vacancy (NV), silicon-vacancy (Si-V), germanium-vacancy (Ge-V), etc. This study delves into the annealing of 175 μm-thick Ge-doped polycrystalline diamond (PCD) films grown by microwave plasma-assisted chemical vapor deposition (MPCVD). Large-area PCD plate was cut into smaller equivalent 5 × 5 mm2 pieces, which were separately subjected to annealing in microwave plasma in H2 atmosphere, to annealing in vacuum or to annealing under high-pressure high-temperature conditions (HPHT, 5.9 GPa, 2000 °C). The structure, phase composition and photoluminescence (PL) of samples before and after various annealing processes were investigated. All applied types of annealing enhance both the Si-V and Ge-V lines in PL at room temperature. Increasing annealing temperature leads to gradual decrease of full widths at half-maxima (FWHM) of diamond Raman peak (1332.5 cm−1), as well as Si-V (738 nm) and Ge-V (602 nm) PL peaks. In addition, the limitations for each type of annealing are established. The obtained results are crucial for the design of CVD-grown Ge-doped and Si-doped PCD materials that can be used for applications in photonics such as single photon sources, biomarkers, as well as for the fabrication of optical diamond thermometers.

High-pressure high-temperature synthesis of NdRe2.

In this study, we report the synthesis of a new cubic neodymium-rhenium metallic alloy NdRe2 through the utilization of high pressure and laser heating in a diamond anvil cell. NdRe2 crystallizes in the space group with a lattice parameter equal to 7.486 (2) Å and Z = 8at 24 (1) GPa and 2,200 (100) K. It was studied using high-pressure single-crystal X-ray diffraction. The compound crystallizes in the cubic MgCu2 structure type. Its successful synthesis further proves that high-pressure high-temperature conditions can be used to obtain alloys holding a Laves phase structure. Ab initio calculations were done to predict the mechanical properties of the material. We also discuss the usage of extreme conditions to synthesize and study materials present in the nuclear waste.

Evaluating the Effect of Claytone-EM on the Performance of Oil-Based Drilling Fluids.

This study provides a detailed characterization and evaluation of Claytone-EM as a rheological additive to enhance the performance of oil-based drilling fluids (OBDFs) under high-pressure, high-temperature (HPHT) conditions. It also offers a comparative evaluation of the effectiveness of Claytone-EM with an existing organoclay, analyzing their mineral and chemical compositions, morphologies, and particle sizes. A series of experiments are performed to evaluate Claytone-EM's influence on crucial drilling mud properties, such as mud density, electrical stability, sagging tendency, rheology, viscoelastic properties, and filtration properties, to formulate a stable and high-performing OBDF. Results indicated that Claytone-EM had no significant impact on mud density but remarkably enhanced emulsion stability. Claytone-EM effectively mitigated sagging issues under both static and dynamic conditions, leading to improvements in the plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and YP/PV ratio. The PV, YP, AV, and YP/PV ratios were improved by 11, 85, 28, and 66% increments, respectively, compared with those of the drilling fluid formulated with MC-TONE. The addition of Claytone-EM resulted in enhancing gel strength and improving the filtration properties of the drilling fluid. The filtration volume was reduced by 2% from 5.0 to 4.9 cm3, and the filter cake thickness had a 13% reduction from 2.60 to 2.26 mm. These findings highlight Claytone-EM as a valuable additive for enhancing OBDF performance, particularly under challenging HPHT conditions. Its ability to provide emulsion stability, reduce static and dynamic sag, and control filtration holds the potential to enhance drilling operations, minimize downtime, and bolster wellbore stability. This study acknowledges certain limitations, including its temperature range, which could benefit from exploration at extreme temperatures. Additionally, the absence of flow experiments limits a comprehensive understanding of sag effects, and further research and field-scale evaluations are recommended to validate and optimize the application of Claytone-EM in OBDFs.

Cr5.7Si2.3P8N24-A Chromium(+IV) Nitridosilicate Phosphate with Amphibole-Type Structure.

The first nitridic analog of an amphibole mineral, the quaternary nitridosilicate phosphate Cr5.7Si2.3P8N24 was synthesized under high-pressure high-temperature conditions at 1400 °C and 12 GPa from the binary nitrides Cr2N, Si3N4 and P3N5, using NH4N3 and NH4F as additional nitrogen source and mineralizing agent, respectively. The crystal structure was elucidated by single-crystal X-ray diffraction with microfocused synchrotron radiation (C2/m, a=9.6002(19), b=17.107(3), c=4.8530(10) Å, β=109.65(3)°). The elemental composition was analyzed by energy dispersive X-ray spectroscopy. The structure consists of vertex-sharing PN4-tetrahedra forming zweier double chains and edge-sharing (Si,Cr)-centered octahedra forming separated ribbons. Atomic resolution scanning transmission electron microscopy shows ordered Si and Cr sites next to a disordered Si/Cr site. Optical spectroscopy indicates a band gap of 2.1 eV. Susceptibility measurements show paramagnetic behavior and support the oxidation state Cr+IV, which is confirmed by EPR. The comprehensive analysis expands the field of Cr-N chemistry and provides access to a nitride analog of one of the most prevalent silicate structures.

Microfluidic devices, materials, and recent progress for petroleum applications: A review

AbstractMicrofluidic devices are miniaturized systems that manipulate fluids on a small scale, typically at microlitre or nanolitre volumes. They have received significant attention in various industries, including petroleum applications. The recent advancements in microfluidic devices for petroleum applications have the potential to improve oil recovery efficiency, reduce costs, and provide valuable insights into fluid behaviour and reservoir characterization. This paper aims to provide a comprehensive overview of microfluidic devices, their materials, and their applications in the petroleum industry. The first section of the paper focuses on describing the materials used in microfluidic devices specifically tailored for energy sector applications. In the second section, the paper highlights relevant applications and discoveries in petroleum research, showcasing the innovative techniques and special features enabled by microfluidic devices. These applications include but are not limited to asphaltenes characterization, enhanced oil recovery (EOR), and water treatment. The versatility and customization of microfluidic devices have allowed researchers to accurately represent fractures, ensure chemical conformance, simulate high pressure–high temperature conditions and reservoir heterogeneity, and study geochemical interaction. Additionally, molecular tagging and machine learning techniques have been employed for image analysis, further enhancing the capabilities of microfluidic devices. Throughout the paper, the advantages and disadvantages of implementing microfluidic devices and utilizing specific materials are thoroughly discussed. This analysis provides valuable insights into the challenges and potential limitations associated with this technology. To conclude, the paper offers suggestions, ideas, and highlights for future research paths, pointing toward promising directions for further exploration in this field.

Carbon Isotopic Behavior During Hydrocarbon Expulsion in Semiclosed Hydrous Pyrolysis of Type I and Type II Saline Lacustrine Source Rocks in the Jianghan Basin, Central China

Abstract Organic carbon isotopic analysis is a significant approach for oil-source correlation, yet organic carbon isotopic behavior during oil expulsion from saline lacustrine source rocks is not well constrained, and this hinders its wide application for fingerprinting oils generated by saline lacustrine source rock. To resolve this puzzle, semiclosed hydrous pyrolysis was conducted on typical saline lacustrine source rocks from the Qianjiang Formation (type I kerogen) and Xingouzui Formation (type II kerogen) sampled in the Jianghan Basin, China, under high-temperature high-pressure conditions (T = 275℃–400℃; P = 65–125 MPa). Experimental results show that there is minor carbon isotopic fractionation (<3‰) between pyrolyzed and nonpyrolyzed retained oil fractions during the main oil generation/expulsion stage of both type I and II source rocks. Carbon isotopic fractionations between expelled and retained oil fractions are also minor (<2‰) during this stage. The δ13C values of retained and expelled oil fractions generated by the type I saline lacustrine source rock correlate positively with the degree of oil expulsion, whereas the influence of oil expulsion on the δ13C values of oil fractions generated by the type II source rock was not consistent. In addition, carbon isotopic analysis also unravels the mixing of oil-associated gases with different maturity levels and/or generated via different processes. Outcomes of this study demonstrate that oil expulsion from type I and II saline lacustrine source rocks cannot be able to result in large-degree carbon isotopic fractionation, indicating that carbon isotopic analysis is a feasible approach for conducting oil-source correlation works in saline lacustrine petroleum systems.

Formation of grain boundaries in nanocrystalline SiC ceramics examined by powder diffraction supported by MD simulations

Atomic structure of nanocrystalline SiC powder sintered under high-pressure high-temperature conditions was examined with use of powder diffraction technique supported by molecular dynamics simulation. Shape and atomic structure of grain surfaces was identified based on real space PDF analysis of experimental diffraction data and theoretical calculations performed for atomic models of nanograins relaxed by MD simulation. It is shown that in presence of about 5 % of excess carbon in the initial 11 nm size SiC powder HPHT sintering leads to formation of (100) and (110) grain boundaries formed by single C planes and (111) boundaries formed by double C planes. Under pressure 3 GPa and 1600°C sphere-like grains tend to transform to dodecahedrons terminated by single C atomic planes. Under 8 GPa the grains adopt the shape of cube-octahedrons terminated by (100) single C planes and (111) planes terminated by single and double C planes.

Exploring the phase change and structure of carbon using a deep learning interatomic potential.

Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though ab initio molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C60 crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy.

Preparation of nanodiamonds based on phase transformation of vertical sheet under atmospheric pressure

A basic and important way to prepare diamond is to make graphite experience the phase transformation under the high-pressure high-temperature (HPHT) condition. However, this method needs stringent equipment and high investment cost. Recently, we proposed a method to prepare the diamond by phase transformation of graphite at atmospheric pressure with monodispersed Ta atoms. It is found that a phase transformation happens to H atoms under atmospheric pressure, but the role of O atoms has not been investigated. Here, we use tantalum wires as Ta source and heat the filaments to prepare vertical graphene containing Ta atoms in hot filament chemical vapor deposition (HFCVD) system. And then the vertical graphene layers are annealed in oxygen-containing environment, and nanodiamonds are obtained by phase transformation from the vertical graphene under atmospheric pressure. The results show that the sample morphologies are the same as the untreated vertical graphene’s, when the annealed ambient air pressure is at 10 Pa and 50 Pa with oxygen atom content of 1.96% and 2.04%, respectively; TEM tests reveal TaC and graphite but no diamond in these samples . Nanodiamond grains with the size range of 2–4 nm are observed in the amorphous carbon region of samples annealed at 100 Pa and 500 Pa air pressure with oxygen atom content increasing to 2.77% and 3.11%, respectively, indicating that oxidation facilitates the phase change from Ta-containing vertical graphene to diamond at atmospheric pressure. When the air pressure of the annealing environment rises to 1000 Pa with the oxygen atom content of 3.54%, the sample is extensively oxidized and the graphite structure is severely damaged,which means that a large number of oxygen atoms tend to disrupt the graphite structure rather than promote the phase change into diamond. These results supply a way to prepare nanodiamond and show the effect of O atoms in the graphite phase transition at atmospheric pressure.

Stabilization Of The CN3 5- Anion In Recoverable High-pressure Ln3 O2 (CN3 ) (Ln=La, Eu, Gd, Tb, Ho, Yb) Oxoguanidinates.

A series of isostructural Ln3 O2 (CN3 ) (Ln=La, Eu, Gd, Tb, Ho, Yb) oxoguanidinates was synthesized under high-pressure (25-54 GPa) high-temperature (2000-3000 K) conditions in laser-heated diamond anvil cells. The crystal structure of this novel class of compounds was determined via synchrotron single-crystal X-ray diffraction (SCXRD) as well as corroborated by X-ray absorption near edge structure (XANES) measurements and density functional theory (DFT) calculations. The Ln3 O2 (CN3 ) solids are composed of the hitherto unknown CN3 5- guanidinate anion-deprotonated guanidine. Changes in unit cell volumes and compressibility of Ln3 O2 (CN3 ) (Ln=La, Eu, Gd, Tb, Ho, Yb) compounds are found to be dictated by the lanthanide contraction phenomenon. Decompression experiments show that Ln3 O2 (CN3 ) compounds are recoverable to ambient conditions. The stabilization of the CN3 5- guanidinate anion at ambient conditions provides new opportunities in inorganic and organic synthetic chemistry.