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ItBu (ItBu = 1,3-di- tert -butylimidazol-2-ylidene) represents the most important and most versatile N -alkyl N-heterocyclic carbene available in organic synthesis and catalysis. Herein, we report the synthesis, structural characterization and catalytic activity of ItOct (I t Octyl), C 2 -symmetric, higher homologues of ItBu. The new ligand class, including saturated imidazolin-2-ylidene analogues has been commercialized in collaboration with MilliporeSigma: ItOct, 929 298; SItOct, 929 492 to enable broad access of the academic and industrial researchers within the field of organic and inorganic synthesis. We demonstrate that replacement of the t -Bu side chain with t -Oct results in the highest steric volume of N -alkyl N-heterocyclic carbenes reported to date, while retaining the electronic properties inherent to N-aliphatic ligands, such as extremely strong σ-donation crucial to the reactivity of N -alkyl N-heterocyclic carbenes. An efficient large-scale synthesis of imidazolium ItOct and imidazolinium SItOct carbene precursors is presented. Coordination chemistry to Au( i ), Cu( i ), Ag( i ) and Pd( ii ) as well as beneficial effects on catalysis using Au( i ), Cu( i ), Ag( i ) and Pd( ii ) complexes are described. Considering the tremendous importance of ItBu in catalysis, synthesis and metal stabilization, we anticipate that the new class of ItOct ligands will find wide application in pushing the boundaries of new and existing approaches in organic and inorganic synthesis. 

The formation of amide bonds represents one of the most fundamental processes in organic synthesis. Transition-metal-catalyzed activation of acyclic twisted amides has emerged as an increasingly powerful platform in synthesis. Herein, we report the transamidation of N-activated twisted amides by selective N–C(O) cleavage mediated by air- and moisture-stable half-sandwich Ni(II)–NHC (NHC = N-heterocyclic carbenes) complexes. We demonstrate that the readily available cyclopentadienyl complex, [CpNi(IPr)Cl] (IPr = 1,3-bis(2,6-diisopropylphenyl)imidazol-2-ylidene), promotes highly selective transamidation of the N–C(O) bond in twisted N-Boc amides with non-nucleophilic anilines. The reaction provides access to secondary anilides via the non-conventional amide bond-forming pathway. Furthermore, the amidation of activated phenolic and unactivated methyl esters mediated by [CpNi(IPr)Cl] is reported. This study sets the stage for the broad utilization of well-defined, air- and moisture-stable Ni(II)–NHC complexes in catalytic amide bond-forming protocols by unconventional C(acyl)–N and C(acyl)–O bond cleavage reactions. 

Breathing biomarkers, such as breathing rate, fractional inspiratory time, and inhalation-exhalation ratio, are vital for monitoring the user's health and well-being. Accurate estimation of such biomarkers requires breathing phase detection, i.e., inhalation and exhalation. However, traditional breathing phase monitoring relies on uncomfortable equipment, e.g., chestbands. Smartphone acoustic sensors have shown promising results for passive breathing monitoring during sleep or guided breathing. However, detecting breathing phases using acoustic data can be challenging for various reasons. One of the major obstacles is the complexity of annotating breathing sounds due to inaudible parts in regular breathing and background noises. This paper assesses the potential of using smartphone acoustic sensors for passive unguided breathing phase monitoring in a natural environment. We address the annotation challenges by developing a novel variant of the teacher-student training method for transferring knowledge from an inertial sensor to an acoustic sensor, eliminating the need for manual breathing sound annotation by fusing signal processing with deep learning techniques. We train and evaluate our model on the breathing data collected from 131 subjects, including healthy individuals and respiratory patients. Experimental results show that our model can detect breathing phases with 77.33% accuracy using acoustic sensors. We further present an example use-case of breathing phase-detection by first estimating the biomarkers from the estimated breathing phases and then using these biomarkers for pulmonary patient detection. Using the detected breathing phases, we can estimate fractional inspiratory time with 92.08% accuracy, the inhalation-exhalation ratio with 86.76% accuracy, and the breathing rate with 91.74% accuracy. Moreover, we can distinguish respiratory patients from healthy individuals with up to 76% accuracy. This paper is the first to show the feasibility of detecting regular breathing phases towards passively monitoring respiratory health and well-being using acoustic data captured by a smartphone.