Second Coordination Sphere Promoted Catalysis: Organometallic Hydrogen Bond Donors for Enantioselective Organic Transformations

This dissertation describes the development of 2-guanidinobenzimidazole (GBI) containing ruthenium based organometallic hydrogen bond donors and their applications in second coordination sphere promoted catalysis (SCSPC). The synperiplanar triad arrangement of the NH donor (D) sites in GBI and derivatives are studied to establish that chelation preorganizes GBI in a DDD motif that is not an energy minimum with the free ligand. Laterhe importance of preorganization is explored in reactions catalyzed by GBI and derivatives. Protonated or methylated BArf (B(3,5-C6H3(CF3)2)4) salts of GBI, 1+BArf– (84%) and 2+ BArf– (58%), are prepared along with the protonated salts ofguanidine and 2-aminobenzimidazole, 3+ BArf– (70% ) and 4+ BArf– (75%),respectively. Refluxing GBI and (η5-C5H5)Ru(PPh3)2(Cl) in toluene forms the chelatedcomplex [(η5-C5H5)Ru(PPh3)(GBI)]+ Cl^- (8+ Cl^-; 96%), which upon addition of COforms [(η5-C5H5)Ru(CO)(GBI)]+ Cl^- (9+ C^-; 91%). Subsequent anion metathesis of 8+and 9+ Cl– gives the respective PF6– and BArf– salts (83-92%). 9+ PF6– can also beprepared from [(η5-C5H5)Ru(CO)(NCCH3)2]+ PF6– (81%). GBI and 9+ Cl– (10 mol%,rt) are ineffective (48 h) for the condensations of 1-methylindole and trans-ß-nitrostyrene (6). In contrast, salts 1-4+ BArf– (25-95%, 1 h) and 8-9+ X– (PF6– andBArf–) are active catalysts (30-97%) under similar conditions. Furthermore, GBI derivatives with a NHR group (GBI-R; R = 16a, CH2Ph; 16b, (SC)-CH(CH3)Ph; 16c, (RCRC)-CH-(CH2)4-CH-NMe2; 16d, (RCRC)-CH-(CH2)4-CH-NCH2(CH2)3CH2) are prepared. Reactions with [(η5-C5H5)Ru(CO)(NCCH3)2]+ PF6–afford the chiral-at-metal chelates [(η5-C5H5)Ru(CO)(GBI-R)]+ PF6– (18a-d+ PF6–, 39-77%). The Ru,C configurational diastereomers of 18c+ PF6– separate upon alumina chromatography (RRuRCRC, >99:01 diastereomer ratio (dr); SRuRCRC, <2:98 dr). Configurations are assigned by CD spectra, DFT calculations, and a crystal structure. Both (SRuRCRC)-18c+ PF6– and (RRuRCRC)-18c+ PF6– (1-10 mol%) catalyze Michael addition reactions between 1,3-dicarbonyl equivalents and 6 in high yields and enantioselectivities (90-99% ee). The free GBI-R ligand exhibits only modest activity. The chiral ruthenium center has little influence over the product configuration. Finally, ruthenium GBI complexes bearing a bulky electron withdrawing pentaphenylcyclopentadienyl ligand are accessed by treating a CH3CN suspension of (η5-C5Ph5)Ru(CO)2(Br) with Me3NO•2H2O, GBI, and Ag+ PF6–. Silica gelchromatography workups lead to [(η5-C5Ph5)Ru(CO)(GBI)]+ PF6– (48+ PF6–; 70%),whereas with alumina [(η5-C5Ph5)Ru(CO)(GBI)]+ BArf– (48+ BArf–; 69%) is obtainedafter anion metathesis. The neutral compound (η5-C5Ph5)Ru(CO)(GBI–H) (49; 72%)bearing a deprotonated GBI ligand (GBI–H) is obtained from 48+ PF6– with K+ t-BuO–.These are characterized by NMR, other spectroscopic methods, and X-ray crystallography. Protonation of 49 with the axially chiral enantiopure phosphoric acid, (P)-Phos-H (HOP(=O)(o-C10H6O)2)), leads to (RRu/SRu)-48+ (P)-Phos– (92%) as amixture of Ru,Axial configurational diastereomers. The diastereomer (SRu)-48+ (P)-Phos– (35%) can be isolated with >98:02 dr from cold toluene/hexane. Subsequent anion metathesis provides (SRu)-48+ BArf– (80%). The absolute configuration is assigned by CD spectroscopy. (SRu)-48+ BArf– (10 mol%) is an efficient catalyst for Friedel-Craftsalkylations and Michael addition reactions even under aerobic conditions. The addition of thiophenol to trans-3-cinnamoyloxazolidin-2-one is highly enantioselective (>99%). The neutral complex 49 is even capable of acting as a multifunctional catalyst and promotes Michael addition reaction of diethyl malonate and 6 in the absence of an external base

Internet of Things Software and Hardware Architectures and Their Impacts on Forensic Investigations: Current Approaches and Challenges

The never-before-seen proliferation of interconnected low-power computing devices, patently dubbed the Internet of Things (IoT), is revolutionizing how people, organizations, and malicious actors interact with one another and the Internet. Many of these devices collect data in different forms, be it audio, location data, or user commands. In civil or criminal nature investigations, the data collected can act as evidence for the prosecution or the defense. This data can also be used as a component of cybersecurity efforts. When data is extracted from these devices, investigators are expected to do so using proven methods. Still, unfortunately, given the heterogeneity in the types of devices that need to be examined, few widely agreed-upon standards exist. In this paper, we look at some of the architectures, current frameworks, and methods available to perform forensic analysis of IoT devices to provide a roadmap for investigators and researchers to form the basis of an investigation

Isothermal low-field tuning of exchange bias in epitaxial Fe/Cr2O3/Fe

Moderate dc magnetic fields of less than 1 T allow tuning the exchange bias in an epitaxially grown Fe 10 nm/Cr2O3 2.7 nm/Fe 10 nm trilayer between negative and positive bias fields. Remarkably, this tunable exchange bias is observed at least up to 395 K which exceeds the Néel temperature of bulk Cr2O3 (307 K). The presence of spontaneous exchange bias and the absence of training effects at room temperature suggest the existence of stable interface moments independent of antiferromagnetic long range order in Cr2O3. Furthermore, the coercivity remains constant, independent of the exchange bias field. In contrast, large training associated with nonequilibrium spin configurations of antiferromagnetically ordered Cr2O3 appears below 50 K

An Effective Transfer Learning Based Landmark Detection Framework For UAV-Based Aerial Imagery Of Urban Landscapes

Aerial imagery captured through airborne sensors mounted on Unmanned Aerial Vehicles (UAVs), aircrafts, satellites, etc. in the form of RGB, LiDAR, multispectral or hyperspectral images provide a unique perspective for a variety of applications. These sensors capture high-resolution images that can be used for applications related to mapping, surveying, and monitoring of crops, infrastructure, and natural resources. Deep learning based algorithms are often the forerunners in facilitating practical solutions for such data-centric applications. Deep learning-based landmark detection is one such application which involves the use of deep learning algorithms to accurately identify and locate landmarks of interest in images captured through UAVs. This study proposes an efficient transfer learning method for feature extraction using a ResNet50 architecture, paired with a FasterRCNN object detection for an automated landmark detection framework. Additionally, a novel technique for hierarchical image annotation and synthetic sampling is also introduced to address the issue of class imbalance. Empirical results prove that our proposed approach outperforms other state-of-the-art landmark detection methodologies compared

Magnetic entropy changes in nanogranular Fe:Ni\u3csub\u3e61\u3c/sub\u3eCu\u3csub\u3e39\u3c/sub\u3e

Artificial environment-friendly Gd-free magnetic nanostructures for magnetic cooling are investigated by temperature-dependent magnetic measurements. We consider two-phase nanocomposites where nanoclusters (Fe) are embedded in a Ni61Cu39 matrix. Several composite films are produced by cluster deposition. The average Fe cluster size depends on the deposition conditions and can be tuned by varying the deposition conditions. The quasiequilibrium Curie temperature of the Fe particles is high, but slightly lower than that of bulk Fe due to finite-size effects. Our experiments have focused on ensembles of 7.7 nm Fe clusters in a matrix with a composition close to Ni61Cu39, which has a TC of 180 K. The materials are magnetically soft, with coercivities of order 16 Oe even at relatively low temperature of 100 K. The entropy changes are modest, –ΔS = 0.05 J/kg K in a field change of 1 T and 0.30 J/kg K in a field change of 7 T at a temperature of 180 K, which should improve if the cluster size is reduced

Magnetic entropy changes in nanogranular Fe:Ni\u3csub\u3e61\u3c/sub\u3eCu\u3csub\u3e39\u3c/sub\u3e

Artificial environment-friendly Gd-free magnetic nanostructures for magnetic cooling are investigated by temperature-dependent magnetic measurements. We consider two-phase nanocomposites where nanoclusters (Fe) are embedded in a Ni61Cu39 matrix. Several composite films are produced by cluster deposition. The average Fe cluster size depends on the deposition conditions and can be tuned by varying the deposition conditions. The quasiequilibrium Curie temperature of the Fe particles is high, but slightly lower than that of bulk Fe due to finite-size effects. Our experiments have focused on ensembles of 7.7 nm Fe clusters in a matrix with a composition close to Ni61Cu39, which has a TC of 180 K. The materials are magnetically soft, with coercivities of order 16 Oe even at relatively low temperature of 100 K. The entropy changes are modest, –ΔS = 0.05 J/kg K in a field change of 1 T and 0.30 J/kg K in a field change of 7 T at a temperature of 180 K, which should improve if the cluster size is reduced