Publications

Abstract Diaryl difluoromethanes are valuable targets for medicinal chemistry because they are bioisosteres of diaryl ethers and can function as replacements for diaryl methane, ketone, and sulfone groups. However, methods to prepare diaryl difluoromethanes are scarce, especially methods starting from abundant aryl halides. We report the Pd‐catalyzed aryldifluoromethylation of aryl halides with aryldifluoromethyl trimethylsilanes (TMSCF 2 Ar). The reaction occurs when the catalyst contains a simple, but unusual, dialkylaryl phosphine ligand that promotes transmetallation of the silane. Computational studies show that reductive elimination following transmetallation occurs with a low barrier, despite the fluorine atoms on the α‐carbon, due to coordination of the difluorobenzyl π‐system to palladium. The co‐development of a cobalt‐catalyzed synthesis of the silanes broadened the scope of the process including several applications to the synthesis biologically relevant diaryl difluoromethanes.

Abstract Benzylic fluorides are obtained by a three‐component coupling of unactivated amino‐alkenes, arylboronic acids, and Selectflour through an oxidative Heck mechanism.

First-principles modeling of a new mixed V/Ti post-spinel phase reveals a possible route to ambient-pressure oxide cathodes exhibiting fast Mg diffusion.

Wetlands are significant contributors to global methane (CH4) emissions, a critical driver of climate change. However, the spatial heterogeneity of CH4 fluxes and the underlying mechanisms within these wetland ecosystems remains largely unexplored. This study examines the heterogeneity of CH4 emissions from different types of wetlands in Estonia and California, USA. The studied wetlands include free surface water treatment wetlands, recently restored peatlands in Estonia and three different restored wetlands in California that differ from each other in salinity level, tidal influence, vegetation, and restoration year. All studied sites except the free surface treatment wetlands are equipped with eddy covariance stations for continuous CO2, H2O, and CH4 measurements (open path LI-7500 and LI-7700 analyzers, LICOR Biosciences). Spatial heterogeneity in methane flux was assessed through static chamber measurements using a LI-7810 trace gas analyzer (LICOR Biosciences). Chamber measurement surveys revealed significant variations among gas measurement points within the eddy tower footprint. Additional parameters such as LAI, water pH, electrical conductivity, dissolved oxygen concentration, temperature, turbidity, salinity, water level, and dissolved gas concentration (dCO2 and dCH4; analyzed in the lab with GC-2014, Shimadzu) were measured from each sampling spot. After measurements, we collected surface sediment samples for soil TN, TOC, TIC, DOC, DIC, and DN analyses. Our results indicate significant variation in CH4 fluxes and soil C and N content within different sampling points and in different ecosystems. In the constructed wetland in Estonia, biweekly measurements from twelve distinct points over two years revealed significant heterogeneity in CH4 fluxes, with peak emissions ranging from 144 mg m-2 d-1 to 254.4 mg m-2 d-1 observed from specific chamber measurement plots. In contrast, the restored peatland showed a lower range of CH4 emissions (0.096 mg m-2 d-1 to 34.6 mg m-2 d-1) observed from six measurement plots. The preliminary measurements conducted in California wetlands also showed a large variation within and between the sites. These findings highlight the complex nature of CH4 flux heterogeneity in wetlands and the critical need for site-specific management strategies. Accurately quantifying and understanding these variations is essential for refining CH4 budgets and developing effective mitigation strategies for greenhouse gas emissions from wetland ecosystems.

We study the notion of consistency between a 3D shape and a 2D observation\nand propose a differentiable formulation which allows computing gradients of\nthe 3D shape given an observation from an arbitrary view. We do so by\nreformulating view consistency using a differentiable ray consistency (DRC)\nterm. We show that this formulation can be incorporated in a learning framework\nto leverage different types of multi-view observations e.g. foreground masks,\ndepth, color images, semantics etc. as supervision for learning single-view 3D\nprediction. We present empirical analysis of our technique in a controlled\nsetting. We also show that this approach allows us to improve over existing\ntechniques for single-view reconstruction of objects from the PASCAL VOC\ndataset.\n

<p>The comprehensive information about all detected lipids</p>