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A family of ansa-permethylindenyl-phenoxy (PHENI*) transition-metal chloride complexes has been synthesized and characterized (1–7; {(η5-C9Me6)Me(R″)Si(2-R-4-R′-C6H2O)}MCl2; R,R′ = Me, tBu, Cumyl (CMe2Ph); R″ = Me, nPr, Ph; M = Ti, Zr, Hf). The ancillary chloride ligands could readily be exchanged with halides, alkyls, alkoxides, aryloxides, or amides to form PHENI* complexes [L]TiX2(8–17; X = Br, I, Me, CH2SiMe3, CH2Ph, NMe2, OEt, ODipp). The solid-state crystal structures of these PHENI* complexes indicate that one of two conformations may be preferred, parametrized by a characteristic torsion angle (TA′), in which the η5system is either disposed away from the metal center or toward it. Compared to indenyl PHENICS complexes, the permethylindenyl (I*) ligand appears to favor a conformation in which the metal center is more accessible. When heterogenized on solid polymethylaluminoxane (sMAO), titanium PHENI* complexes exhibit exceptional catalytic activity toward the polymerization of ethylene. Substantially greater activities are reported than for comparable PHENICS catalysts, along with the formation of ultrahigh-molecular-weight polyethylenes (UHMWPE). Catalyst–cocatalyst ion pairing effects are observed in cationization experiments and found to be significant in homogeneous catalytic regimes; these effects are also related to the influence of the ancillary ligand leaving groups in slurry-phase polymerizations. Catalytic efficiency and polyethylene molecular weight are found to increase with pressure, and PHENI* catalysts can be categorized as being among the most active for the controlled synthesis of UHMWPE.

ABSTRACTProstate cancer has heterogeneous growth patterns, and its prognosis is the poorest when it progresses to a neuroendocrine phenotype. Using bioinformatic analysis, we evaluated RNA expression of neuroendocrine genes in a panel of five different cancer types: prostate adenocarcinoma, breast cancer, kidney chromophobe, kidney renal clear cell carcinoma and kidney renal papillary cell carcinoma. Our results show that specific neuroendocrine genes are significantly dysregulated in these tumors, suggesting that they play an active role in cancer progression. Among others, synaptophysin (SYP), a conventional neuroendocrine marker, is upregulated in prostate adenocarcinoma (PRAD) and breast cancer (BRCA). Our analysis shows that SYP is enriched in small extracellular vesicles (sEVs) derived from plasma of PRAD patients, but it is absent in sEVs derived from plasma of healthy donors. Similarly, classical sEV markers are enriched in sEVs derived from plasma of prostate cancer patients, but weakly detectable in sEVs derived from plasma of healthy donors. Overall, our results pave the way to explore new strategies to diagnose these diseases based on the neuroendocrine gene expression in patient tumors or plasma sEVs.

Field tests in a discrete rock fracture validated a combined inert/adsorbing tracer test method to estimate the contact area between fluids circulating through a fracture and the bulk rock matrix (i.e., flow‐wetted surface area, A). Tracer tests and heat injections occurred at a mesoscale well field in Altona, NY. A subhorizontal bedding plane fracture ∼7.6 m below ground surface connects two wells separated by 14.1 m. Recovery of the adsorbing tracer cesium was roughly 72% less than the inert tracer iodide. Using an advection‐dispersion‐reaction model in one‐dimension, the adsorbing/inert tracer method identified substantial flow channelization. These results are consistent with Ground Penetrating Radar (GPR) and thermal sensors. All characterization methods suggest circulating fluids were concentrated in a narrow, 1–2 m wide channel directly connecting the injection and production well. The inert/adsorbing tracer method identified two flow channels with areas of 28 and 80 m2. A one‐dimensional heat transport model predicted production well temperature rises 20.5°C in 6 days, whereas measured temperature rise was 17.6°C. For comparison, two‐dimensional heat transport through a fracture of uniform aperture (i.e., homogeneous permeability) predicted roughly 670 days until production well temperature would rise 17.6°C. This suggests that the use of a fracture of uniform aperture to predict heat transport may drastically overpredict the thermal performance of a geothermal system. In the context of commercial geothermal reservoirs, the results of this study suggest that combined inert/adsorbing tracer tests could predict production well thermal drawdown, leading to improved reservoir monitoring and management. Combined inert/adsorptive tracer testing accurately predicts heat transportConventional hydraulic and tracer tests fail to accurately estimate fracture apertureChanneled flow caused rapid heat transport between an injector/producer well pair

Multicomponent groundwater tracer tests were conducted in a well‐characterized field site in Altona, NY using inert carbon‐cored nanoparticles and a thermally degrading phenolic compound. Experiments were conducted in a mesoscale reservoir consisting of a single subhorizontal bedding plane fracture located 7.6 m below ground surface contained between two wells separated by 14.1 m. The reservoir rock, initially at 11.7°C, was heated using 74°C water. During the heating process, a series of tracer tests using thermally degrading tracers were used to characterize the progressive in situ heating of the fracture. Fiber‐Optic Distributed Temperature Sensing (FODTS) was used to measure temperature rise orthogonal to the fracture surface at 10 locations. Recovery of the thermally degrading tracer's product was increased as the reservoir was progressively heated indicating that the advancement of the thermal front was proportional to the mass fraction of the thermally degrading tracer recovered. Both GPR imaging and FODTS measurements reveal that flow was reduced to a narrow channel which directly connected the two wells and led to rapid thermal breakthrough. Computational modeling of inert tracer and heat transport in a two‐dimensional discrete fracture demonstrate that subsurface characterization using inert tracers alone could not uniquely characterize the Altona field site. However, the inclusion of a thermally degrading tracer may permit accurate subsurface temperature monitoring. At the Altona field site, however, fluid‐rock interactions appear to have increased reaction rates relative to laboratory‐based measurements made in the absence of rock surfaces. A narrow channel dominated flow as confirmed via thermal sensors, ground penetrating radar, and numerical modelsInert tracers alone produced nonunique estimates of fracture aperture distributions and heat transportThermally degrading tracer tests indicated the advancement of an induced thermal front

Predicting the thermal performance of an enhanced geothermal system (EGS) requires a comprehensive characterization of the underlying fracture flow patterns from practically available data such as tracer data. However, due to the inherent complexities of subsurface fractures and the generally insufficient geological/geophysical data, interpreting tracer data for fracture flow characterization and thermal prediction remains a challenging task. The present study aims to tackle the challenge by leveraging a data assimilation method to maximize the utilization of information inherently contained in tracer data, and meanwhile maintain the flexibility to handle various uncertainties. A tracer data interpretation framework was proposed with the following three components integrated: (a) We use principal component analysis (PCA) to reduce the dimensionality of model parameter space. (b) We use ES‐MDA (ensemble smoother with multiple data assimilation) to invert for fracture aperture/flow fields and obtain posterior model ensembles for uncertainty quantification. Various data types are assimilated jointly to improve the predictive ability of the posterior ensemble. (c) The inverted fracture aperture fields are then incorporated into reservoir models to predict thermal performance. We developed a field‐scale EGS model to verify the ability of the framework to characterize highly heterogeneous fracture aperture/flow fields and predicting thermal performance. We also applied the framework to a mesoscale field experiment to demonstrate its potential application in real‐world geothermal reservoirs. The results indicate that the proposed framework can effectively retrieve fracture flow information from tracer data for thermal prediction and uncertainty quantification, and thus provide informative guidance for EGS optimization and risk management. Proposed a data assimilation framework to predict thermal performance of enhanced geothermal system (EGS) from tracer dataDemonstrated the efficacy of the framework through a synthetic field‐scale EGS model and a mesoscale field testCombining conservative and sorptive tracer data offers more accurate prediction of thermal performance than conservative tracer data alone Proposed a data assimilation framework to predict thermal performance of enhanced geothermal system (EGS) from tracer data Demonstrated the efficacy of the framework through a synthetic field‐scale EGS model and a mesoscale field test Combining conservative and sorptive tracer data offers more accurate prediction of thermal performance than conservative tracer data alone

Artificial tracer testing is an effective technique to identify fluid flow pathways and characterize subsurface hydrological properties. Synthetic DNA tracers, available in virtually unlimited number of unique variations, enable multiwell tracer testing and have the potential to improve the characterization of groundwater flowpaths enormously. This study investigated the effect of DNA length (i.e., number of base pairs) and adsorption on DNA tracer transport via well‐controlled laboratory experiments. Nine unique synthetic double‐stranded DNA tracers were designed with varying lengths (90–200 base pairs) and flowed with reference solute tracers (bromide and lithium) through columns packed with glass beads or Ottawa sand. The peak arrivals of DNA tracers were earlier than bromide in the glass‐bead column test, yet significantly retarded in the sand column test. In a given porous medium, DNA length did not have noticeable effects on peak arrival or tracer dispersion. However, DNA tracer recovery increased with increasing DNA length in the glass‐bead column test but decreased with increasing DNA length in the sand column test, likely resulting from the corresponding trends in partition coefficients as revealed by tracer transport modeling. Such reversed trends in different porous media likely resulted from the different likelihoods for longer DNA to have multisegment attachment during transport. Our findings suggest that both DNA length and the surface properties of the porous medium have notable effects on DNA tracer transport and hence need to be considered carefully in future tracer test designs in terms of site selection, DNA sequence design and data interpretation. Compared with bromide, DNA tracers had faster breakthrough times in a glass‐bead column but delayed breakthrough in a silica sand columnThe adsorption partition coefficient increased in proportion to DNA length in the sand column but decreased in the glass‐bead columnDNA tracers with the same length but distinct sequences had the same transport characteristics in a given porous medium Compared with bromide, DNA tracers had faster breakthrough times in a glass‐bead column but delayed breakthrough in a silica sand column The adsorption partition coefficient increased in proportion to DNA length in the sand column but decreased in the glass‐bead column DNA tracers with the same length but distinct sequences had the same transport characteristics in a given porous medium

Identifying fluid flow maldistribution in planar geometries is a well‐established problem in subsurface science/engineering. Of particular importance to the thermal performance of enhanced (or “engineered”) geothermal systems is identifying the existence of nonuniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a genetic algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two‐dimensional Hele‐Shaw geometry (i.e., parallel plates). The inverse problem is solved by minimizing the L2norm between simulated residence time distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C‐Dot nanoparticle). Principal component analysis (PCA) of spatially correlated permeability fields enabled reduction of the parameter space by more than a factor of 10 and restricted the inverse search to reservoir‐scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding‐plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. The results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements. Joint pressure‐tracer calibration is necessary to identify reservoir short‐circuiting in a single planar fractureMachine learning (genetic algorithm) accurately forecasts rapid heat transfer measured in mesoscale experiments (Altona Field Laboratory)Principal component analysis reduced the number of solved variables by more than a factor of 10

The identification of natural fractures and the wells they connect is crucial for the development of geological reservoirs because it may have an important impact on reservoir model construction and hydraulic fracture propagation. In this study we investigated the use of a novel data source, the microbial community composition in the reservoir formation fluids, for identification of interwell connectivity caused by natural fractures. We verified this concept at a newly developed mesoscale enhanced geothermal system (EGS) field testbed located 4,850 ft (1,478 m) beneath ground surface at the Sanford Underground Research Facility in Lead, SD. Fluids produced at or near the EGS testbed were sampled and subjected to high‐throughput 16S rRNA gene amplicon sequencing to analyze the microbial community profile therein. Despite the typically substantial heterogeneity across the community profiles of samples spatially distributed (10 m to 1.9 km apart) throughout the site, samples from two wells at the EGS testbed showed highly similar microbial community composition, suggesting the two wells intersected the same natural fracture. This evidence of natural connectivity between the two wells at the EGS testbed was later corroborated by core log analysis and sewer camera surveys into the boreholes. Besides the field case described in this study, microbial community composition as a reservoir diagnostic tool would be applicable in a much broader context such as unconventional hydrocarbon exploitation, groundwater reservoir characterization, and environmental remediation, adding valuable “hard” data capable of pinpointing the origins of fluids unambiguously. Indigenous reservoir formation fluids were distinguishable from foreign drilling fluids by their disparate microbial community compositionWells intersecting the same natural fracture produced fluids with similar microbial community profile, revealing interwell connectivityMicrobial community composition pinpoints the identity/origin of reservoir fluids with better specificity than geochemistry data