Ru(II)-Catalyzed Decarboxylative (4 + 2)-Annulation of Benzoic Acids and Benzamides with Propargyl Cyclic Carbonates.

Propargyl cyclic carbonates have emerged as versatile precursors in synthetic chemistry. However, their reactivity has so far been limited to transition metal-catalyzed substitution and cyclization reactions. Herein, we illustrate the successful employment of propargyl cyclic carbonates as coupling partners in Ru(II)-catalyzed C-H annulation of benzoic acids and benzamides. This approach allowed us to access a broad range of biologically relevant isocoumarin and isoquinolinone derivatives in good to excellent yields, utilizing bench-stable and easily accessible precursors. Preliminary mechanistic studies indicated that the C-H metalation step is both reversible and rate-determining in the reaction pathway. Furthermore, the utility of the developed methodology has been illustrated by scale-up and postfunctionalization experiments.

Pyrazolidinone-Aided Ru(II)-Catalyzed Regioselective C-H Annulation with Allenes.

Regioselective annulation of allenes via C-H activation represents an elegant synthetic approach toward the construction of valuable scaffolds. Considering the importance of allenes, herein we developed an unprecedented Ru(II)-catalyzed highly regioselective redox-neutral C-H activation/(4 + 1)-annulation of 1-arylpyrazolidinones employing allenyl acetates to access pyrazolo[1,2-a]indazol-1-one derivatives. Additionally, allenyl cyclic carbonates, which were never tested in C-H activation, were utilized to construct a similar class of heterocycles having a pendent alcohol functionality. Notably, double C-H functionalization was achieved by a simple modification of reaction conditions. The synthetic significance of this methodology is underscored by late-stage modification of natural products, broad substrate scope, gram-scale synthesis, and postfunctionalizations.

Edge-based graph neural network for ranking critical road segments in a network.

Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network's efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm.

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

Physics-Guided Real-Time Full-Field Vibration Response Estimation from Sparse Measurements Using Compressive Sensing.

In civil, mechanical, and aerospace structures, full-field measurement has become necessary to estimate the precise location of precise damage and controlling purposes. Conventional full-field sensing requires dense installation of contact-based sensors, which is uneconomical and mostly impractical in a real-life scenario. Recent developments in computer vision-based measurement instruments have the ability to measure full-field responses, but implementation for long-term sensing could be impractical and sometimes uneconomical. To circumvent this issue, in this paper, we propose a technique to accurately estimate the full-field responses of the structural system from a few contact/non-contact sensors randomly placed on the system. We adopt the Compressive Sensing technique in the spatial domain to estimate the full-field spatial vibration profile from the few actual sensors placed on the structure for a particular time instant, and executing this procedure repeatedly for all the temporal instances will result in real-time estimation of full-field response. The basis function in the Compressive Sensing framework is obtained from the closed-form solution of the generalized partial differential equation of the system; hence, partial knowledge of the system/model dynamics is needed, which makes this framework physics-guided. The accuracy of reconstruction in the proposed full-field sensing method demonstrates significant potential in the domain of health monitoring and control of civil, mechanical, and aerospace engineering systems.

Synthesis of novel polyazacryptands for recognition of tetrahedral oxoanions and their X-ray structures.

The Mannich reaction of tris(pyrrolyl-α-methyl)amine (H3tpa) with a mixture of formaldehyde and primary amine hydrochloride (methyl or ethyl) gave two novel macrobicyclic molecules: a hexapyrrolic pentaazacryptand and an unusual pentapyrrolic tetraazacryptand, which were separated by column chromatography and their structures were determined by X-ray diffraction method. The analogous reaction using benzylamine hydrochloride yielded the encapsulated chloride anion complex in 16% yield even after neutralization with aqueous K2CO3, which was also characterized by X-ray diffraction. In addition, the anion binding properties of these macrobicycles were investigated by NMR titration methods, and the binding constants for halides and oxoanions were determined with the EQNMR program. The cavity of the hexapyrrolic pentaazacryptand is flexible and large enough enabling it to form inclusion complexes with the smaller size fluoride ion as well as with the bulkier oxoanions. This was demonstrated by (19)F NMR spectroscopy and by the X-ray structures of the encapsulated sulfate, phosphate, and arsenate ion complexes. Upon complexation the distance between the bridgehead nitrogen atoms changes. Further, anion induced conformational changes were observed in the structures of the oxoanion complexes, particularly in the arsenate structure which represents the first azacryptand encapsulated structure of an arsenate ion. Furthermore, the competition crystallization experiment showed that the phosphate ion complex of the hexapyrrolic pentaazacryptand is the sole crystallization product from an aqueous-organic medium, as confirmed by IR and powder X-ray diffraction studies.

Azatripyrrolic and azatetrapyrrolic macrocycles from the Mannich reaction of pyrrole: receptors for anions.

Azatripyrrolic 1 and azatetrapyrrolic 2 macrocycles were synthesized in a single step by the Mannich reaction of pyrrole in the presence of primary amine hydrochloride and were structurally characterized among several other higher analogue azapyrrolic macrocycles. Binding constants for the halide anion complexes are determined by (1)H NMR titrations and they show different binding stoichiometries.