Formation of the Imide [Ta(NMe₂)₃(μ-NSiMe₃)]₂ through an Unprecedented α-SiMe₃ Abstraction by an Amide Ligand

Ta­(NMe₂)₄[N­(SiMe₃)₂] (<b>1</b>) undergoes the elimination of Me₃Si-NMe₂ (<b>2</b>), converting the −N­(SiMe₃)₂ ligand to the NSiMe₃ ligand, to give the imide “Ta­(NMe₂)₃(NSiMe₃)” (<b>3</b>) observed as its dimer <b>4</b>. CyNCNCy captures <b>3</b> to yield guanidinates Ta­(NMe₂)_3–<i>n</i>(NSiMe₃)­[CyNC­(NMe₂)­NCy]_<i>n</i> [<i>n</i> = 1 (<b>5</b>), 2 (<b>6</b>)]. The kinetic study of α-SiMe₃ abstraction in <b>1</b> gives Δ<i>H</i>^⧧ = 21.3(1.0) kcal/mol and Δ<i>S</i>^⧧ = −17(2) eu

Reactions of Group 4 Amide Guanidinates with Dioxygen or Water. Studies of the Formation of Oxo Products

Reactions of the zirconium amide guanidinates (R₂N)₂M­[^<i>i</i>PrNC­(NR₂)­N^<i>i</i>Pr]₂ (R = Me, M = Zr, <b>1</b>; M = Hf, <b>2</b>; R = Et, M = Zr, <b>3</b>) with O₂ or H₂O give products that are consistent with the oxo dimers {M­(μ-O)­[^<i>i</i>PrNC­(NR₂)­N^<i>i</i>Pr]₂}₂ (R = Me, M = Zr, <b>4</b>; M = Hf, <b>5</b>; R = Et, M = Zr, <b>6</b>) and polymers {M­(μ-O)­[^<i>i</i>PrNC­(NR₂)­N^<i>i</i>Pr]₂}_<i>n</i> (R = Me, M = Zr, <b>7</b>; M = Hf, <b>8</b>; R = Et, M = Zr, <b>9</b>). Mass spectrometric (MS) analyses of the reactions of water in air with <b>1</b> and <b>2</b> show formation of the Zr monomer Zr­(O)­[^<i>i</i>PrNC­(NMe₂)­N^<i>i</i>Pr]₂ (<b>10</b>), oxo dimers <b>4</b> and <b>5</b>, and dihydroxyl complexes M­(OH)₂[^<i>i</i>PrNC­(NMe₂)­N^<i>i</i>Pr]₂ (M = Zr, <b>11</b>; Hf, <b>12</b>). Similar MS analyses of the reaction of diethylamide guanidinate <b>3</b> with water in air show the formation of Zr­(O)­[^<i>i</i>PrNC­(NEt₂)­N^<i>i</i>Pr]₂ (<b>13</b>), Zr­(OH)₂[^<i>i</i>PrNC­(NEt₂)­N^<i>i</i>Pr]₂ (<b>14</b>), <b>6</b>, and {(Et₂N)­Zr­[^<i>i</i>PrNC­(NEt₂)­N^<i>i</i>Pr]₂}⁺ (<b>15</b>). Kinetic studies of the reaction between <b>1</b> and a continuous flow of 1.0 atm of O₂ at 80–105 °C indicate that it follows pseudo-first-order kinetics with Δ<i>H</i>^⧧ = 8.7(1.1) kcal/mol, Δ<i>S</i>^⧧ = −54(3) eu, Δ<i>G</i>^⧧_358 K = 28(2) kcal/mol, and a half-life of 213(1) min at 85 °C

Hot Carrier Cooling Mediated Efficiency Enhancement in Diamine Passivated Perovskite Solar Cells

Slowing hot carrier (HC) cooling in halide perovskites emerges as an efficient strategy to enhance the power conversion efficiency (PCE) of perovskite solar cells (PSCs). This approach facilitates the rapid extraction of HCs before they dissipate within the intricate lattice of the perovskite structure. In this investigation, we delve into the impact of amine-based additives on the relaxation of HCs in perovskite solar cells using a diamine 4,4′-diaminodiphenylmethaneiodide salt (MDA). The diamine has been used as an in situ additive in the perovskite precursor. The ultrafast femtosecond transient absorption (fs-TA) spectroscopy study confirms that the diamine-modified film slows the HC cooling time to 672 fs from the control (538 fs). Moreover, MDA additive helps to improve crystallization and passivate the traps for inhibiting nonradiative recombination, leading to higher PCE compared to the control device. The passivated devices show impressive ambient stability and retain 80% of initial PCE after 500 h. Our study provides an in-depth understanding of how precise control of HC cooling through additive engineering can improve the PSC’s efficiency and stability

A Spatial Modeling Framework to Evaluate Domestic Biofuel-Induced Potential Land Use Changes and Emissions

We present a novel bottom-up approach to estimate biofuel-induced land-use change (LUC) and resulting CO₂ emissions in the U.S. from 2010 to 2022, based on a consistent methodology across four essential components: land availability, land suitability, LUC decision-making, and induced CO₂ emissions. Using high-resolution geospatial data and modeling, we construct probabilistic assessments of county-, state-, and national-level LUC and emissions for macroeconomic scenarios. We use the Cropland Data Layer and the Protected Areas Database to characterize availability of land for biofuel crop cultivation, and the CERES-Maize and BioCro biophysical crop growth models to estimate the suitability (yield potential) of available lands for biofuel crops. For LUC decisionmaking, we use a county-level stochastic partial-equilibrium modeling framework and consider five scenarios involving annual ethanol production scaling to 15, 22, and 29 BG, respectively, in 2022, with corn providing feedstock for the first 15 BG and the remainder coming from one of two dedicated energy crops. Finally, we derive high-resolution above-ground carbon factors from the National Biomass and Carbon Data set to estimate emissions from each LUC pathway. Based on these inputs, we obtain estimates for average total LUC emissions of 6.1, 2.2, 1.0, 2.2, and 2.4 gCO2e/MJ for Corn-15 Billion gallons (BG), <i>Miscanthus × giganteus</i> (MxG)-7 BG, Switchgrass (SG)-7 BG, MxG-14 BG, and SG-14 BG scenarios, respectively