Ismaila Sy
email: ismaila.sy@uadb.edu.sn

Birahime Diouf
email: birahime.diouf@uadb.edu.sn

Abdou Khadre Diop
email: abdoukhadre.diop@uadb.edu.sn

Cyril Drocourt
email: cyril.drocourt@u-picardie.fr

David Durand
email: david.durand@u-picardie.fr

Enhancing Security in Connected Medical IoT Networks through Deep Learning-Based Anomaly Detection

Keywords: Cybersecurity, Deep learning, IDS, IoT, Medical Data. 1

In recent years, there has been an alarming increase in cyberattacks targeting connected medical devices. Distributed denial of service (DDoS) and botnet attacks are particularly common, and many vulnerabilities in IoT systems make these devices particularly vulnerable. Traditional intrusion detection techniques often fall short in addressing these threats. To overcome this challenge, we propose a deep learning-based intrusion detection system (IDS) for connected medical devices that utilizes four different architectures: multilayer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and hybrid CNN-LSTM. We evaluated our system on the UNSW-NB15 and Edge-IIoTset datasets, and achieved a classification accuracy of 99.8% for binary classification and 96% for multiclass classification, with a false alarm rate of less than 2%. Our results show that deep learning can be an effective tool for detecting fraud attacks in connected medical devices. This research aims to enhance the security posture of medical IoT systems and mitigate potential risks.

Introduction

Every second, 127 new devices connect to the Internet for the first time (McKinsey Digital). The number of connected devices is following an exponential curve, rising from 15 billion in 2015 to 30 billion in 2020 and 75 billion in 2025 (Statista). This curve is likely to increase even faster with the arrival of 5G wireless technology, developed in part for the IOT world and enabling deployment in private networks. The business sectors concerned by IoT are wide-ranging: in 2018, they were mainly geared towards smart cities (23%), followed by connected industry (17%), with healthcare accounting for just 6% (iot-analytics.com), but growing fast. In 2019, 86% of companies in the healthcare sector were using connected objects (Comparitech, i-SCOOP). By 2020, according to Forbes, 646 million IoT devices will be in use in hospitals, clinics and doctors' surgeries. In the medical IoT domain, connected networks enable real-time patient monitoring, seamless data collection, and enhanced healthcare delivery. However, this integration also poses inherent security risks that pose significant threats to patient confidentiality, data integrity, and overall network security. Implementing IoT security measures and finding suitable storage solutions will be the top priorities in the near future. To mitigate these risks, robust security measures are imperative [START_REF]Internet of Things (IoT): Technologies, Applications, Challenges and Solutions[END_REF]. Traditional intrusion detection methods, such as rule-based IDSs and signature-based IDSs, have shown their limitations in effectively detecting and adapting to new threats emerging in dynamic IoT environments. This inherent limitation prompts the exploration of more sophisticated and adaptive solutions, among which deep learning stands out. Deep learning, renowned for its exceptional capabilities in pattern recognition and anomaly detection tasks, emerges as a promising avenue to address the evolving challenges posed by the intricacies of IoT security.

In this paper, we make a significant contribution by employing a diverse set of deep learning methods for enhancing security in connected medical IoT networks. Specifically, we utilize three powerful deep learning models: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to implement our anomaly detection approach. This multi-model strategy allows us to leverage the strengths of different architectures, ensuring a comprehensive coverage of potential threats.

Our primary research objective is twofold: first, to achieve a high level of accuracy in identifying security threats in medical IoT networks, and second, to minimize the false positive rate. Through extensive experiments using real-world data (Edge-IIoTset), we have demonstrated high performance. The results display precision rates of 99.8% in binary classification and 96% in multi-class classification. This underscores the efficacy of our deep learning-based approach in accurately identifying and classifying security threats in the complex and dynamic medical IoT environment. Moreover, our work stands out in its success in substantially reducing false alarm rates. By leveraging advanced deep learning techniques, we have significantly enhanced the precision of our intrusion detection system, ensuring that security alerts are more reliable and actionable. This reduction in false positives is critical in preventing unnecessary disruptions and resource wastage, making our approach not only accurate but also highly practical for real-world deployment.

In summary, our research contributes to advancing the state-of-the-art in connected medical IoT network security by employing a multi-model deep learning approach that not only achieves exceptional accuracy but also significantly reduces false alarm rates. This innovative methodology holds great promise for safeguarding patient data and improving the overall quality of healthcare delivery in the rapidly evolving digital landscape. In the upcoming sections, we conduct a comprehensive review of the security issues within connected medical IoT networks and the current methods for identifying anomalies. Ultimately, we summarize the primary outcomes and suggest potential paths for future research to bolster security in linked medical IoT networks.

Literature Review

Numerous researchers and experts have recognized the gravity of the situation and are actively studying the security aspects of IoMT. Johnson [START_REF] Johnson | The Internet of Things in healthcare: Prospects for the future[END_REF] emphasized the need to address security concerns in the context of the Internet of Things in healthcare. Uslu [START_REF] Uslu | Security and privacy aspects of the Internet of Medical Things (IoMT)[END_REF] delved into the specific security and privacy implications of the Internet of Medical Things, underscoring the urgency of robust protection measures. Zeadally et al. [START_REF] Zeadally | Security of the Internet of Things: A review of the state of the art[END_REF] provided an extensive survey on IoT security, encompassing IoMT as a critical domain. Additionally, Kocabas et al. [START_REF] Kocabas | Internet of Medical Things (IoMT): A comprehensive survey on recent advancements and future directions[END_REF] conducted a comprehensive review of recent advancements and future directions in IoMT, with a particular focus on security implications. Ferrag et al. [START_REF] Ferrag | Federated deep learning for cybersecurity in the internet of things: Concepts, applications, and experimental analysis[END_REF] have conducted a deep learning investigation for intrusion detection. They evaluates the models performances based on accuracy, false alarm rates, and detection rates. The CNNs showed more satisfactory performances than the FFN (Feedforward Neural Network) and RNN (Recurrent Neural Network). Odetola et al. [START_REF] Odetola | A scalable multi-label classification to deploy deep learning architectures for edge devices[END_REF] develop a multi-label classification method using a CNN on edge IoT devices. They also discuss recent research on IoT security, focusing on intrusion detection techniques based on neural network strategies. Tian et al. [START_REF] Tian | A distributed deep learning system for web attack detection on edge devices[END_REF] proposed a distributed approach to identify cyber threats via URLs using deep learning algorithms. Their framework can be practically effective due to its automated collection functions, ease of upgradeability and reliability in defending against attacks on distributed deep models. In a big data situation, Hassan et al. [START_REF] Hassan | A hybrid deep learning model for efficient intrusion detection in big data environment[END_REF] have concocted a way to determine break-ins that involve a combination of a Weight-Dropped Long Short-Term Memory (WDLSTM) model and a CNN model. Liu et al. [START_REF] Liu | Intrusion detection of imbalanced network traffic based on machine learning and deep learning[END_REF] proposed a hybrid approach that combines data sampling, cost-sensitive learning, ensemble learning, and deep learning for intrusion detection in imbalanced network traffic. In [START_REF] Vu | Deep transfer learning for IoT attack detection[END_REF], the authors propose an approach based on Deep Transfer Learning (DTL) for IoT attacks detection. They have performed three series of experiments on recent datasets and the experimental results show that the DTL model brings an improvement on the accuracy of detection compared to classical approaches. Kandhro et al. [START_REF] Kandhro | Detection of real-time malicious intrusions and attacks in IoT empowered cybersecurity infrastructures[END_REF] presents a generative adversarial network for detecting cyber threats in IoT-driven IIC networks. Their approach is evaluated with the KDDCup99, NSL-KDD and UNSW-NB15 datasets. The results show an increase in performance between 95 % and 97 % in terms of accuracy, reliability and efficiency in detecting numerous attacks. [START_REF] Saheed | Efficient Cyber Attack Detection on the Internet of Medical Things-Smart Environment Based on Deep Recurrent Neural Network and Machine Learning Algorithms[END_REF] propose a novel intrusion detection system (IDS) for the Internet of Medical Things (IoMT) using a deep recurrent neural network (DRNN) and supervised machine learning (SML) models. They first preprocess and normalize the network data, and then optimize the features using a bio-inspired particle swarm algorithm. Finally, they evaluate the proposed DRNN and SML models on a standard intrusion detection dataset. The results show that the proposed SML model outperforms existing approaches with an accuracy of 99.76%.

While experiments have undoubtedly made great strides in developing intrusion detection systems, the ongoing challenge lies in finding a delicate balance of accuracy and reducing false positives. Striking the optimal balance of accurately identifying real threats and minimizing false positives is an ongoing pursuit in cybersecurity. This challenge highlights the importance of optimizing intrusion detection methods to minimize the impact of false alarms on system performance and to ensure strong protection against cyber threats.

Methodological Approach: Deep Learning-Based Anomaly Detection for Strengthening Security in Connected Medical IoT Networks

In this section, we discuss the datasets and methods we use. We will evaluate the performance of the proposed approaches by considering evaluation metrics commonly used for deep learning algorithms, such as recall, accuracy, F1 score, detection rate, and false alarm rate.

Datasets selection

A good dataset is crucial for studying cyberattacks on IoT devices. By using diverse dataset, encompassing both hybrid traffic and specific IoT dataset, we can capture a wide range of attack vectors and scenarios, enhancing its robustness and generalizability. For our study, we utilized a hybrid dataset known as UNSW-NB15, which offers a comprehensive representation of various network traffic scenarios. This hybrid dataset incorporates both normal and attack data instances, making it a valuable resource for training and evaluating intrusion detection systems [START_REF]The UNSW-NB15 dataset[END_REF]. In addition to this, we employed the Edge-IIoTset dataset. This dataset focuses on IoT-specific network traffic, allowing us to investigate and enhance the security of connected IoT networks effectively.

Harnessing Deep Learning Algorithms to Safeguard Medical IoT Networks

In the exhilarating era of cyber infrastructure, where challenges in cybersecurity reach new heights, the exploration of vast data reservoirs in networks, operating systems, and information realms demands avant-garde solutions. Deep learning, a true catalyst of machine learning, emerges as the key for Intrusion Detection Systems (IDS), whether rooted in classical signatures or exploring anomalies. Plunging into the realms of classification and prediction, deep learning provides an immediate response by identifying unusual cybernetic patterns, anticipating not only ongoing attacks but also future threats, thereby redefining digital vigilance. With remarkable ingenuity, deep learning meets the escalating demands of Big Data, establishing a robust defense where highperforming IDS minimizes false alarms. In this captivating panorama, our approach highlights the use of deep learning in IDS, incorporating innovative approaches such as MLP, LSTM, CNN, and the hybrid CNN-LSTM, propelling proactive detection of malicious activities in an ever-evolving digital era. This wave of innovation holds crucial importance in the medical domain, accentuating the relevance of its applications and underscoring the critical necessity of resilient cybersecurity in this highly sensitive field.

A. Multi-layer Perceptrons (MLPs)

Deep Neural Networks (DNNs) constitute a set of neurons organized into a sequence of multiple layers known as Multi-layer Perceptrons (MLPs). They distinguish themselves from traditional Artificial Neural Networks (ANNs) through their depth and the number of layers and nodes (neurons) comprising the network. When an ANN has two or more hidden layers, it is referred to as a deep neural network [START_REF] Yin | IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset[END_REF]. These networks aim to model data containing intricate architectures by combining various non-linear transformations (Fig. 1). Rosenblatt1 introduced the fundamental concept of the perceptron in 1958. The perceptron calculates a single output from multiple real-valued inputs (xi) by forming a linear combination based on its input weights (w) and then passing the output through a nonlinear activation function. Mathematically, this can be expressed as follows: . CNNs outperform classical machine learning algorithms, achieving significant success in computer vision tasks. They find wide applications in image and video processing, natural language processing (NLP), recommendation systems, and more. Convolutional networks are particularly effective due to specialized layers, including convolutional layers, pooling layers, and fully connected layers (Fig. 2).

𝑦 = 𝛿 (∑ 𝑊

Fig. 2. Architectural of a Convolutional Neural Network Model

C. Long Short-Term Memory units (LSTMs)

The RNN has a long time step as it considers the previously stored state when updating the weights. However, as training progresses, gradients become smaller, and after a few steps, errors may fail to propagate to the end of the network. This result in a negligible difference in the outcome, preventing weight updates. This issue with RNN is known as vanishing gradients. To overcome this problem, a Long Short-Term Memory (LSTM) architecture was proposed in the mid-1990s by German researchers Sepp Hochreiter and Juergen Schmidhuber for recurrent neural networks. Additionally, Gated Recurrent Units (GRU) were introduced to further address the vanishing gradients problem (Fig. 3). Both LSTM and GRU architectures function similarly, but GRU uses fewer training parameters, requiring less memory and training faster than LSTMs. However, LSTMs tend to be more accurate on datasets with longer sequences [START_REF] Laghrissi | Intrusion detection systems using long short-term memory (LSTM)[END_REF].

Fig. 3. Architectural Overview of LSTM and GRU Models

D. Hybrid CNN-LSTM network

A hybrid CNN-LSTM network is a combined architecture that integrates Convolutional Neural Network (CNN) layers for feature extraction with Long Short-Term Memory (LSTM) layers for sequence modeling and prediction (Fig. 4). This hybrid design is often employed in tasks such as visual time series prediction, where CNNs excel at capturing spatial features, and LSTMs handle temporal dependencies, offering a comprehensive approach to complex data patterns and activity recognition [START_REF] Tasdelen | A hybrid CNN-LSTM model for pre-miRNA classification[END_REF]. In this study, we introduce four distinct models: a MLP, CNN, LSTM and a hybrid CNN-LSTM model. Initially, we conduct binary classification, which involves identifying whether the network traffic is normal or malicious. Additionally, we delve into multi-class classification, where we classify the traffic type into either normal or various attack categories, such as DOS, DDOS, or MITM (Man-In-The Middle) attacks (Fig. 5). This comprehensive approach allows us to accurately categorize and analyze the network traffic patterns for enhanced intrusion detection. In summary, our methodology involved a dual-step classification process, first determining the normality of traffic versus maliciousness and then identifying the precise type of attack. Utilizing four models, including MLP, LSTM, CNN, and CNN-LSTM, we harnessed their strengths to discern between legitimate and malicious activities while achieving fine-grained attack categorization. This comprehensive approach not only enhances the detection accuracy but also equips cybersecurity practitioners with valuable insights for effective threat mitigation and network defense.

Fig. 6. Confusion Matrix for Multiclass Classification

The multiclass classification results (Table 2) underscore the model's commendable performance across various attack categories. Notably, the model achieves flawless identification of "Normal" traffic, demonstrating perfect accuracy, recall, and F1-score.

The "DDoS" class also exhibits high accuracy and recall, indicative of the model's ability to effectively recognize instances, although the F1-score suggests some instances of misclassification. Challenges arise in the "Code Injection" class, where lower accuracy and F1-score suggest difficulty in precise identification, highlighting an area for potential improvement. In the "Malware" class, the model shows good accuracy but a lower recall, suggesting opportunities to enhance identification sensitivity. The "Information Theft" class demonstrates a balanced and robust performance across metrics. The "MITM" class achieves flawless classification. Importantly, the confusion matrix (Fig. 6) reveals no instances of attacks misclassified as normal and vice versa, affirming the model's reliability in distinguishing between normal and malicious activities. While the model performs admirably, targeted improvements in specific classes could further enhance its overall classification capabilities.

Conclusion

In

  𝑖 𝑛 𝑛=1 𝑥 𝑖 + 𝑏) = 𝛿(𝑊 𝑇 𝑋 + 𝑏) the weight vector.  X: the input vector.  b: represents the bias.  δ: denotes the activation function A multilayer perceptron network (MLP) consists of a set of source nodes forming an input layer, a hidden layer of one or more compute nodes, and an output layer of nodes. The input signal propagates through the network layer by layer. The signal flow in such a network with hidden layers is shown in Equation (1). Deep neural networks (DNN) are often used for supervised learning problems. During model training (learning), all weights and biases are set to optimal values.

Fig. 1 .

 1 Fig. 1. The Layered Architecture of Deep Neural Networks (DNN)

Fig. 4 .

 4 Fig. 4. Hybrid CNN-LSTM architecture

Fig. 5 .

 5 Fig. 5. Anomaly Detection Architecture

  Wang et al. propose an anomaly detection model tailored for Industrial Control Systems (ICS), employing a deep residual Convolutional Neural Network (CNN). The model strategically incorporates transfer learning to identify unknown attacks, thereby minimizing training time[START_REF] Wang | Anomaly detection of industrial control systems based on transfer learning[END_REF]. Nevertheless, it is important to note that the model relies on a dataset containing known attacks for effective operation. In order to address the limitations of traditional machine learning techniques in detecting and classifying attacks in IoT networks, a novel anomaly-based intrusion detection model using deep learning, specifically convolutional neural networks (CNNs), is proposed by[START_REF] Ullah | Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks[END_REF]. The proposed model is implemented in 1D, 2D, and 3D, and is evaluated using four different IoT intrusion detection datasets. Transfer learning is also used to implement binary and multiclass classification using a pre-trained CNN model. The proposed model achieves high accuracy, precision, recall, and F1 score compared to existing deep learning implementations.

To enhance the security of edge networks, L. Nie et al.

[START_REF] Nie | Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach[END_REF]

propose a deep learningbased intrusion detection algorithm based on the generative adversarial network (GAN). Their method includes three phases: feature selection, deep learning architecture design, and intrusion detection model combination. The proposed method achieves high accuracy in detecting multiple attacks, suggesting that GAN-based deep learning is a promising approach for intrusion detection in edge networks.

To identify IoT devices and/or IP addresses that are compromised by a Botnet attack, E. Gelenbe and M. Nakıp

[START_REF] Gelenbe | Traffic Based Sequential Learning During Botnet Attacks to Identify Compromised IoT Devices[END_REF]

propose a novel online Compromised Device Identification System (CDIS). CDIS uses an Auto-Associative Dense Random Neural Network (AADRNN) to train itself online during normal operation, using traffic metrics measured as traffic arrives. Experimental evaluation on publicly available Mirai Botnet attack data shows that CDIS achieves high performance with Balanced Accuracy of 97%, despite its low on-line training and execution time. Y. K. Saheed et al.

Table 1 .

 1 Binary Classification Models PerformanceIn the Multi-Class Classification phase, we focused on the challenging task of classifying network traffic into multiple categories. By classifying the data into these six categories, we can gain a better understanding of the different types of attacks present in the dataset and develop more targeted and effective security measures to mitigate these threats. This reclassification provides a clearer and more concise representation of the diverse range of attacks encountered in real-world network traffic, enabling us to enhance the detection and prevention of cyber threats.

		Model	Accuracy	Validation accuracy	Loss
	LSTM_UNSW-NB15	93,00	94,00	0.13
	MLP_Edge-IIoTset	99,99	99,80	2.67e-04
	LSTM_Edge-IIoTset	99,99	99,99	9.98e-05
	CNN_Edge-IIoTset	99,99	99,87	0.16
	CNN-LSTM_Edge-IIoTset	99,98	99,97	1.13e-04
	4.2	Multi-Class Classification

Table 2 .

 2 Multi-Class Classification Performances

	Accuracy	Recall	F1-score	Support
	Normal	1.00	1.00	1.00	409200
	DDoS	0.91	0.99	0.95	86434
	Code Injection	0.66	0.87	0.75	30810
	Malware	0.94	0.49	0.65	25094
	Information Theft	0.95	0.74	0.83	21257
	MITM	1.00	1.00	1.00	107
	In binary classification experiences (Table 1), we evaluated several classification mod-
	els for binary network traffic analysis. The LSTM_UNSW-NB15 model demonstrated
	good performance with an accuracy of 93.98% on the test dataset, and an even higher
	accuracy of 94.99% during validation. The MLP_Edge-IIoTset model displayed re-
	markable accuracy, achieving a perfect 99.99% accuracy on the test dataset and main-
	taining a very high accuracy of 99.98% during validation. The model's exceptionally
	low loss value of 2.62e-04 indicates its successful ability to distinguish between normal
	and malicious network traffic. Additionally, we observed outstanding performance
	from the LSTM_Edge-IIoTset model, which achieved high accuracy of 99.99% both
	on the test dataset and during validation. For the CNN_Edge-IIoTset model, an accu-
	racy of 99.98% on the test dataset and a validation accuracy of 99.97% were achieved,
	with a relatively small loss value of 0.1676, demonstrating robust performance in clas-
	sifying normal and malicious traffic. Lastly, the CNN_LSTM_Edge-IIoTset model at-
	tained a perfect accuracy of 99.98% on both the test dataset and during validation. The
	model's low loss value of 1.1334e-04 further validates its effectiveness in accurately
	classifying network traffic.

  this study, we present a deep learning based IoT traffic classification. We explore binary and multi-class classifications, differentiating normal and malicious network traffic, and identifying specific attack types within IoT networks. Experimental results validate the efficacy of real-world datasets like Edge-IIoTset for robust model training. However, challenges persist, including the intricacies of IoT ecosystems, model adaptability, and vulnerabilities to adversarial attacks. Looking ahead, our research suggests integrating deep learning with steganography and Blockchain for more resilient intrusion detection. Incorporating edge computing and federated learning could enhance real-time data processing and privacy. In conclusion, our deep learning approach strengthens IoT cybersecurity, bolstering intrusion detection and safeguarding patient data and healthcare systems. This study paves the way for future research to develop comprehensive solutions, addressing challenges and contributing to a safer IoT landscape.

Frank Rosenblatt was an American psychologist who worked on artificial intelligence. As a prominent figure in the "neural network" movement, which aimed to construct artificial intelligence based on the design of the human neural network, he developed the perceptron in 1957 at Cornell University.


Marc Devillard

Iker Chiara Dinoi

Iker Del

Clément Orione

Marie Cordier

Gilles Alcaraz

Del Rosal

The Missing Link in the Pd-Catalyzed Annulation of Alkynes for the Silirene-to-Silole Transformation

published or not. The documents may come   L'archive ouverte pluridisciplinaire

■ INTRODUCTION

The chemical behavior of three-membered ring heterocycles is mostly dictated by the release of the ring strain 1 as the main driving force, with a strong influence both of the nature of the embedded heteroelement and of the degree of unsaturation of the cycle. 2 They have proven to be molecules of choice in organic synthesis, offering a wide range of reactivity and a versatile chemistry, especially when combined with catalysis. 3 In particular, they enable the synthesis of higher heterocyclic structures either via intramolecular ring expansion or through insertion reactions in the presence of an unsaturated partner. This approach provides an attractive methodology for the installation of the heteroelement, particularly in the course of new π-conjugated molecules incorporating heavier p-block elements. [START_REF]Cycloaddition with Use of (Aminosilyl)boronic 1140 Esters as Synthetic Equivalents of Silylene[END_REF] In this context, the chemistry of silacycles (silicon rings with 3, 5 4, 6 5, 6d,7 or more 8 members) is probably the more mature research field driven by that of silacyclopentadienes (siloles) and their peculiar electronic properties. 9 Over the years, this interest has turned siloles into important synthetic targets and has motivated significant efforts toward methodological development. 10 While preparation of siloles relied generally on uncatalyzed transformations involving stoichiometric amounts of organometallic reagents, most recent achievements are based on catalytic routes, mainly using rhodium or palladium complexes. 11 They proceed either (i) intramolecularly from acyclic silanes through arylations of Si-X (X = CH 3 , H) bonds 12 and C-C bond formation between peripheral substituents 13 or (ii) intermolecularly by reaction of an alkyne with a suitable silicon-based reagent. 14 Within this second category, the use of silacyclopropene (silirene) as a direct precursor of siloles is a very attractive strategy, as it takes advantage of both the inherent reactivity of the silicon-containing precursor and the possibilities offered by accepted mechanism remains, however, partly hypothetical due to the lack of compelling evidence, particularly in the form of isolated organometallic intermediates and their subsequent reactivity. On this basis, and in order to gain information on the mechanism, we performed a series of experiments with silirene 1 at the catalytic and stoichiometric levels by combining catalysis and coordination chemistry. We were indeed able to isolate and fully characterize the first intermediate I derived from the activation of the silirene and study its fate upon reaction with an alkyne by multinuclear NMR spectroscopy. In addition, extensive DFT calculations enabled us to explain and corroborate our results, providing a better description of the elementary steps, the nature of the higher-energy intermediates, and ultimately the whole process.

■ RESULTS AND DISCUSSION Palladium-Catalyzed Reaction with Silirene 1 and Acetylene. The reactivity of silirenes with alkynes under palladium-catalyzed conditions is intimately associated with steric factors, and their behavior strongly depends on the substitution pattern both at the silicon and at the carbon atoms of the three-membered ring heterocycle but also on the alkyne involved in the reaction. [START_REF] Ishikawa | The Chemistry of Silacyclopropenes[END_REF] In that respect, silirene 1 does not evolve in solution in the presence of [Pd(PPh 3 ) 4 ], even upon prolonged heating at 100 °C. Si,Si-Di-tert-butyl silirenes 16,[START_REF] Herz | Application of multifunctional silylenes and siliranes as universal crosslinkers for metal-free curing of silicones[END_REF] display enhanced kinetic stability, and no disilacyclohexadiene compounds of type A resulting from the silirene [3 + 3]cyclodimerization 21 [Scheme 2�route (1)] could be observed in our case. This trend was confirmed by the reaction of 1 with acetylene (1 bar) under catalytic conditions. In that case, neither disilacyclohexadiene compounds B [Scheme 2�route (4)] nor bicyclic compounds of type C [Scheme 2�route (5)] as in the case of 1,1-dimethyl-2,3-bis(trimethylsilyl)silirene were detected. [START_REF] Seyferth | Silacyclopropenes. 3. Palladium-catalyzed insertion reactions[END_REF] The corresponding silole 2 was obtained as f1 the sole product (Figure 1) and isolated in 80% isolated yield after purification by flash chromatography on silica gel. Single crystals of 2 could be obtained from a saturated pentane solution at room temperature, and their structure was determined by X-ray diffraction analysis.

Steric and Electronic Effects of the Alkyne on the

Annulation Reaction. The influence of both the steric and the electronic nature of the terminal alkyne substrate was also investigated in order to get insight into the mechanism and to explain the exclusive chemo and regioselectivity observed with 1. First, this study suggests that steric factors play a more prominent role than we initially thought in our case. This could be confirmed by the reaction of tert-butylacetylene and trimethylsilylacetylene that bear quaternary substituents directly linked to the triple bond. While the two substrates revealed total unreactivity under the standard catalytic conditions, the tert-butyl substituted silole 3 could be finally formed selectively under forcing conditions (100 °C, 72 h., cat. loading 10 mol %) and isolated in 82% yield after column f2 chromatography on silica gel (Figure 2). 3 could be fully characterized by NMR, HRMS, as well as X-ray diffraction analyses.

In a second stage, the electronic effects of the alkyne substituents on the catalytic process were assessed by using 3, 152 bottom). 22 153 To gain further information on the mechanism of the 154 reaction and more particularly on the coordination of the 155 alkyne to the palladium in the catalytic process, the kinetic 156 isotope effect (KIE) associated with a possible C-H bond 157 cleavage of the alkyne moiety was evaluated. The study was 158 conducted independently with protio and deutero phenyl-159 acetylene, Ph-C�C-H, and Ph-C�C-D, respectively, affording the previously described silole 5 and its isotopomer 196

-OMe > -H > -C(O)CH3 > -C(O)H ∼ -CF 3 (Figure
The reaction monitoring at 50 °C by 31 P{ 1 H} NMR of 1 197 with both complexes independently revealed the appearance of 198 a new species displaying a resonance signal at δ 80.9 (R = tBu) 199 and δ 25.3 (R = oTol) in addition to the remaining starting 200 materials. The conversion to the product was estimated to be 201 40% and 82% for R = tBu and R = oTol, respectively. 26 In the 202 29

Si{ 1 H} NMR spectrum, new singlet resonance signals at δ 203 -33.7 (R = tBu) and δ -31.6 (R = oTol), low field shifted 204 regarding the starting material (δ -69.9), were observed. 205 Except for the special case of titanasilacyclobutenes E R that 206 exhibit a peculiar deshielded signal for the Ti-bound Si atom, 207 the observed values are downfield shifted compared to the 208 values from the other group 10 metallacyclobutenes C, D, and 209 F R (Figure 4). 27 This feature could be attributed to the nature 210 of the exocyclic silicon SiRR' substituents. Despite this 211 discrepancy, the spectroscopic data from the full NMR 212 characterization are consistent with a 1,2-palladacyclobutene 213 structure for 6-PtBu 3 and 6-P(oTol) 3 with a monophosphine-214 coordinated palladium center. Unfortunately, attempts to 215 isolate any of the two compounds by crystallization gave solid Pd(PR 3 ) n (n = 2, 3) complexes. NMR analysis of the mother liquors revealed only the presence of 1 in solution with minor decomposition products. This result is in agreement with previous NMR spectroscopic analyses (vide supra) and again suggests that the OA process leading to 6-PtBu 3 and 6-P(oTol) 3 is an equilibrated process. In view of these results, we reasoned that the excess phosphine ligand in solution could be the cause of the difficulties encountered in isolating complex 6.

We hypothesized that this point could be circumvented by the use of a "phosphine-free" palladium(0) precursor in the presence of a stoichiometric amount of a chosen phosphine.

We then turned to the use of the thermally unstable Both complexes 6-PPh 3 and 6-P(OPh) 3 react readily with phenylacetylene at room temperature (t ≈ 5 and 30 min, respectively) to form selectively the model silole 5 as observed in the implemented catalytic conditions. Interestingly, the same reaction with 6-COD is much slower and gives a mixture of 5 and an acyclic structural isomer 5′ in 50 and 28% isolated yield, respectively (see S20 for details). The structure of 5′ was determined thanks to HRMS and multinuclear NMR analyses.

In particular, the 3 J SiH coupling constant of 6.9 Hz indicates that the central olefin has retained its stereochemistry with the thienyl groups in the cis position. 32 These results strongly support the involvement of a The computed natural charges indicate a positive charge of 393 1.65 for the electropositive Si center, together with a slight 394 polarization of the C1-C2 bond, with the C1 carbon carrying 395 a more negative charge (-0.54) than the C2 carbon (-0.23). 396 The computed molecular orbitals responsible for the bonding 397 within the Si-C1-C2-Pd cycle are shown in Figure 8. The 398 HOMO, HOMO-4, HOMO-7, HOMO-10, and HOMO-18 399 orbitals describe the sigma bonding system of the four-400 membered 1,2-palladasilacyclobutene skeleton [START_REF]HOMO-18 orbital, finally, displays the overlapping of a C2 sp 1301 hybridized orbital with both a Pd d orbital on one side and a C1 sp 1302 hybridized orbital on the other side, together with an additional slight 1303 bonding interaction between the same C1 sp hybridized orbital and a 1304 Si p orbital. (37) 1305 For sake of clarity, the LUMO is also depicted in Figure S3-1306 DFT. It consists of an antibonding interaction between the Si and Pd 1307 atoms together with a bonding interaction between Pd and 1308 phosphorus. (38) 1309[END_REF]37 (Figure 8), 401 whereas the HOMO-1 and the HOMO-17 orbitals account for 402 the π interaction between C1 and C2 (see Figure S129 in the 403 Supporting Information). No orbital overlapping is detected 404 between the Si and C2 atoms, despite the large SiC2 distance 405 observed experimentally in the X-ray structures of 6 (vide 406 supra), ruling out any possibility of bonding between these two 407 centers.

Reductive Elimination

Step. From species F in Figure 7, an easy reductive elimination process may then occur with a kinetic barrier of 10.4 kcal mol -1 , leading to the exothermic formation of the experimentally observed silole product (G = -47.6 kcal mol -1 ). The relative orientation of the two thienyl substituents is likely to play an important role when tetracoordinate Pd species are involved. This is related to the well- Starting from the alkyne π-complex D2, indeed, the 456 phenylacetylene insertion becomes a one-step process with 457 an associated kinetic barrier of 38.2 kcal mol -1 (TS-D2F2), 458 which cannot compete with the pathway starting from D, 459 displaying a kinetic barrier of 18.8 kcal mol -1 (TS-EF).

460

Electronic and Steric Effects in the Insertion

Step of 461 the Alkyne. In line with the experimental observations, we 462 computed the annulation reaction of different para-substituted 463 ethynylbenzenes R-pC 6 H 4 -C�C-H (R = CF 3 and OMe). 464 As shown in Figures S138-S141 in the Supporting 465 Information, compared to the phenylacetylene analogues, the 466 energies of intermediates D_CF3 and D_OMe differ by less 467 than 2 kcal mol -1 (5.9 and 7.5 kcal mol -1 , respectively, 468 compared to 7.1 kcal mol -1 for D) and the energies of alkyne 469 insertion transition states, TS-DE_CF 3 , TS-EF_CF 3 and TS-470 DE_OMe, TS-EF_OMe by less than 4 kcal mol -1 (8.8 and 471 10.1 kcal mol -1 compared to 9.8 kcal mol -1 for TS-DE and 472 16.5 and 19.7 compared to 18.8 kcal mol -1 for TS-EF). 473 According to the literature, 39 these values are of the same order 474 of magnitude as the accuracy of the DFT method used here, 475 indicating that the electronic effects of the alkyne substituents 476 observed experimentally are too small to be rationalized by 477 DFT methods. The same conclusion applies to the effect of 478 PPh 3 and P(OPh) 3 phosphines. As shown in the comparison of 479 the enthalpy profiles of the Pd-catalyzed annulation reaction of 480 Ph-C�C-H with silirene 1 in the presence of PPh 3 and 481 P(OPh) 3 (Figure S142), the effect of phosphine on the 482 reaction and, more particularly, on the stabilization of the resulting mixture was stirred for 3 days at 100 °C, giving a bright yellow mixture. The solution was concentrated to dryness, and the residue was purified by column chromatography on silica gel [SiO 2 15 g, eluent petroleum ether (100%), R f = 0.60], giving the expected compound as a white solid in 82% yield. Single crystals suitable for Xray diffraction were obtained from a saturated solution of pentane at -30 °C.

1 H NMR (CD 2 Cl 2 , 400 MHz): δ (ppm) 0.99 (s, 9H, CH 3 SitBu), 1.05 (s, 9H, CH 3 tBu), 1.18 (s, 9H, CH 3 SitBu), 2.01 (s, 3H, CH 3 -Th), 2.05 (s, 3H, CH 3 -Th), 2.30 (s, 3H, CH 3 -Th), 2.33 (s, 3H, CH 3 -Th), 5.94 (s with 29 Si sat., 1H, 2 J HSi = 10.5 Hz, H vinyl), 6.41 (s, 1H, H Th), 6.51 (s, 1H, H Th); 13 C{ 1 H} NMR (CD 2 Cl 2 , 100 MHz): δ (ppm) 14.7 (s, CH 3 -Th), 15.1 (s, CH 3 -Th), 15.3 (s, CH 3 -Th), 15.7 (s, CH 3 -Th), 19.6 (s, C SitBu), 19.9 (s, C SitBu), 28.9 (s, CH 3 SitBu), 29.5 (s, CH 3 SitBu), 30.6 (s, CH 3 tBu), 37.2 (s, C tBu), 121.4 (s, CH vinyl), 126.6 (s, CH Th), 128.6 (s, Cquat.), 129.0 (s, CH Th), 132.5, 133.2, 134.7, 138.6 and 138.7 (s, Cquat.) Compound 4a-f: General Procedure. To a solution of silirene 1 (50.0 mg, 0.129 mmol) and alkyne (0.129 mmol, 1.00 equiv) in toluene (1.6 mL) was added palladium tetrakis(triphenylphosphine) (7.4 mg, 6.4 × 10 -3 mmol, 5.0 mol %) under stirring, and the resulting mixture was stirred for 24 h at 50 °C, giving a bright yellow solution. The volatiles were then removed under reduced pressure giving an oily residue that was purified by column chromatography on silica gel (see underneath for chromatography conditions for each compound). These compounds were obtained in quantitative yield, but the aldehyde 4d that partially decomposes on silica gel was isolated in only 22% yield.

Compound 4a (R = OMe). After flash chromatography on silica gel {SiO 2 , 15 g; gradient DCM/pentane (10/90) → DCM/pentane (20/ 80); R f [DCM/pentane (20/80)] = 0.60}, the product was obtained as a white solid. Single crystals suitable for X-ray diffraction were obtained from a saturated diethylether solution at -20 °C.

1 H NMR (CD 2 Cl 2 , 400 MHz): δ (ppm) 1.09 (s, 9H, CH 3 tBu), 1.14 (s, 9H, CH 3 tBu), 1.74 (s, 3H, CH 3 -Th), 1.79 (s, 3H, CH 3 -Th), 2.20 (s, 3H, CH 3 -Th), 2.39 (s, 3H, CH 3 -Th), 2.89 (s, 3H, OCH 3 ), 5.96 (s, 1H, H Th), 5.99 (s with 29 Si sat., 1H, 2 J HSi = 11.2 Hz, H vinyl), 6.50 (d, 2H, 3 J HH = 8.9 Hz, H Ph), 6.64 (s, 1H, H Th), 6.89 (d, 2H, 3 J HH = 8.9 Hz, H Ph); 13 C{ 1 H} NMR (CD 2 Cl 2 , 100 MHz): δ (ppm) 14.5 (s, CH 3 -Th), 14.6 (s, CH 3 -Th), 15.2 (s, CH 3 -Th), 15.4 (s, CH 3 -Th), 19.9 (s, C tBu), 20.1 (s, C tBu) Th), 2.18 (s, 3H, CH 3 -Th), 2.39 (s, 3H, CH 3 -Th), 3.75 (s, 6H, N(CH 3 ) 2 ), 5.92 (s, 1H, H Th), 6.06 (s with 29 Si sat., 1H, 2 J HSi = 11.0 Hz, H vinyl), 6.64 (s, 1H, H Th), 6.67 (d, 2H, 3 J HH = 8.8 Hz, H Ph), 6.94 (d, 2H, 3 J HH = 8.8 Hz, H Ph); 13 C{ 1 H} NMR (CD 2 Cl 2 , 100 MHz): δ (ppm) 14.5 (s, CH 3 -Th), 14.6 (s, CH 3 -Th), 15.1 (s, CH 3 -Th), 15.4 (s, CH 3 -Th), 19.9 (s, C tBu), 20.1 (s, C tBu), 29.0 (s, CH 3 tBu), 29.3 (s, CH 3 tBu), 55.5 (s, N(CH 3 ) 2 ), 112.9 (s, CH Ph), 127.1 (s, CH Th), 127.8 (s, CH vinyl), 127.9 (s, CH Th), 129.1 (s, CH Ph), Compound 6-COD. THF (2 mL) was added to a neat mixture of silirene 1 (30.0 mg, 7.72 × 10 -2 mmol) and (1,5-cyclooctadiene)bis(trimethylsilylmethyl)palladium(II) (30.0 mg, 7.72 × 10 -2 mmol, 1.00 equiv), and the resulting solution was stirred for 1 h at room temperature, giving a dark solution. The solvent was evaporated under vacuum, and the residue thus obtained was further dried for 10 h under vacuum, giving a sticky brown residue. The residue was extracted with pentane (2 mL), and the pentane solution was filtered and placed at -30 °C, giving the expected compound as colorless blocks in 41% yield. Single crystals suitable for X-ray diffraction analysis were obtained in the same conditions. The volatiles were then eliminated under reduced pressure, and the residue was then dried under vacuum (2 × 10 -2 mmbar) for 10 h, giving a green solid. This solid was then washed two times with pentane (2 times 1 mL), giving the expected compound as a clear yellow powder in 65% yield (51 mg). All the attempts to grow single crystals of 6-PPh 3 for X-ray diffraction analysis furnished crystals of the known complex [Pd(PPh 3 ) 3 ]. [START_REF]Cycloaddition with Use of (Aminosilyl)boronic 1140 Esters as Synthetic Equivalents of Silylene[END_REF] H NMR (THF-d 8 , 400 MHz): δ (ppm) 1.04 (s, 18H, CH 3 tBu), 1.86 (s, 3H, CH 3 -Th), 2.13 (s, 3H, CH 3 -Th), 2.32 (s, 3H, CH 3 -Th), 2.34 (s, 3H, CH 3 -Th), 6.56 (s, 1H, H Th), 6.58 (s, 1H, H Th), 7. 31-7.43 (m, 9H, H pPh and H mPh), 7.54-7.64 (m, 6H, H oPh); Compound 6-P(OPh) 3 . To a solution of silirene 1 (100 mg, 0.257 mmol) and triphenylphosphite (67.4 μL, 79.8 mg, 0.257 mmol, 1.00 equiv) in THF (7 mL) was added (1,5-cyclooctadiene)bis-(trimethylsilylmethyl)palladium(II) (100 mg, 0.257 mmol, 1.00 equiv) as a powder in one portion, and the resulting mixture was stirred for 2 h at room temperature, giving a clear yellow solution.

The crude mixture was then filtered, and the resulting solution was concentrated to dryness over 30 min at room temperature. The 891 resulting residue was extracted with pentane (15 mL) at room 892 temperature and filtered, and the yellow pentane solution was placed 893 in the freezer at -40 °C, affording colorless crystals in a few hours.

894

The mother liquor was then eliminated via a syringe, and the crystals 895 were washed 4 times with -40 °C cold pentane (4 times 5 mL). The 896 crystals were then dried under vacuum at 0 °C for 5 h. NMR analysis 897 of the crystalline material revealed the presence of about 5% of 898 [Pd(P(OPh) 3 ) 3 ] in addition to the expected compound (46% yield). 899 Attempts to separate the two complexes were unsuccessful. Crystals of 900 6-P(OPh) 3 suitable for X-ray diffraction were obtained from a 901 saturated THF/pentane solution at -30 °C. 17-7.24 (m, 3H, H pPh), 7.35-7.45 (m, 12H, H oPh and H 913 mPh); 13 C{ 1 H} NMR (THF-d 8 , 100 MHz, 233 K): δ (ppm) 14.4 (s, 914 CH 3 -Th), 14.7 (s, CH 3 -Th), 14.9 (s, CH 3 -Th), 15.3 (s, CH 3 -Th),

  s4 5-D (Scheme 4). No KIE could be observed from the rate of formation, suggesting that acetylenic C-H bond cleavage either does not occur at all or is not involved in the ratedetermining step of the reaction. Additionally, and as observed by the group of Palmer and Woerpel, the reaction is clearly limited to terminal alkynes. 16a Our attempts to react 1 with 1-phenyl-1-propyne left the starting materials unchanged even under forcing conditions (Scheme 4, bottom). OA of Palladium to Silirene 1. At that stage, we turned to coordination chemistry studies in order to address the question of palladasilacycles of type I (Scheme 3) in the palladiumcatalyzed annulation reaction. 1,2-metallasilacyclobutenes are organometallic complexes with an unsaturated four-membered cyclic structure where a transition metal center is stabilized by two sigma bonds of different nature, σ-C-M and σ-Si-M, respectively. Although often invoked, 6c,16b,23 these complexes are rare and limited in number. A first description was reported by Ishikawa 24 in the case of nickelasilacyclobutene C generated f4 from a silirene and [(PEt 3 ) 4 Ni(0)] (Figure 4). It revealed instability and was only characterized in situ by NMR spectroscopy. Since then, other examples have appeared (D, E R , and F R ), 25 but only four of them have been structurally characterized so far in the case of Pt (D), Ti (E H ), and Ni (F H and F CH2OMe ) (Figure 4). They are either obtained by the [2 +

Figure 1 .

 1 Figure 1. Reactivity of 1 with acetylene under Pd-catalyzed conditions, giving silole 2, and X-ray structure of 2 with a displacement ellipsoid plot at a 50% probability level; non-vinylic hydrogens are omitted for clarity. Selected bond lengths [Å] and angles [°]: Si1-C12 1.872(2), Si1-C9 1.9005(19), C11-C12 1.343(3), C10-C11 1.491(3), C9-C10 1.369(3), C9-C17 1.474(3), C10-C23 1.477(3); C1-Si1-C9 115.53(9), C1-Si1-C5 116.62(9), C1-Si1-C12 108.51(9), C5-Si1-C9 109.74(8), C5-Si1-C12 111.91(9), C9-Si1-C12 91.86(9).

Figure 2 .

 2 Figure 2. Synthesis of silole 3 by Pd-catalyzed annulation of 1 and tert-butylacetylene under forcing conditions (left). X-ray structure of 3 (right) with a displacement ellipsoid plot at a 50% probability level; hydrogen atoms are omitted for clarity. Selected bond lengths [Å] and angles [°]: Si1-C9 1.8871(18), Si1-C12 1.8537(19), C9-C10 1.359(2), C10-C11 1.528(2), C11-C12 1.349(3), C9-C17 1.485(2), C10-C23 1.491(2); C1-Si1-C9 113.17(8), C1-Si1-C5 116.98(8), C1-Si1-C12 106.41(8), C5-Si1-C9 111.67(8), C5-Si1-C12 114.81(9), C9-Si1-C12 90.84(8).

Figure 3 .

 3 Figure 3. Synthesis of siloles 4a-f by Pd-catalyzed annulation of parasubstituted phenylacetylenes with 1 (top) and the plot of the catalytic transformations over time (down) for R = OCH 3 , H, C(O)CH 3 , CF 3 , and C(O)H monitored by 1 H NMR spectroscopy (C 6 D 6 , internal standard DCE).

  Scheme 4. Control Experiments of 1 with Protio-, Deuterophenylacetylene, and 1-Phenyl-1-propyne

  s5 195 are known to easily undergo OA reactions (Scheme 5).

  [(COD)Pd(CH 2 TMS) 2 ] dialkyl complex 28 as the convenient precursor of the active 14 electron [(COD)Pd] transient complex by simple reductive elimination of (TMS-CH 2 ) 2 f5 (Figure 5, top). 29 A clean reaction occurs between 1 and [(COD)Pd(CH 2 TMS) 2 ] in THF at room temperature (Figure 5, right), as shown by the 1 H NMR monitoring and the appearance of a singlet resonance signal at δ -34.8 in the 29 Si{ 1 H} NMR in agreement with the values obtained in the case of 6-PtBu 3 and 6-P(oTol) 3 . After work-up, 6-COD could be isolated as colorless crystals in 41% yield, and its structure was determined by X-ray diffraction analysis. In the solid state, the palladium is in a distorted square-planar environment with an acute C16Pd1Si1 angle of 63.75(8)°[Σ α (LPdL) = 360°]. The Si1Pd1 distance of 2.3237(9) Å is in the range of previously characterized silyl palladium(II) complexes (2.25-2.43 Å). Within the four-membered metallacycle, the C9Si1 bond of 1.859(3) Å is slightly elongated relative to the endocyclic Si-C bonds in 1 (1.82 Å in average), whereas the Si1C16 distance of 2.327(3) Å largely exceeds the sum of

Figure 4 .Figure 5 .

 45 Figure 4. Previously described 1,2-metallasilacyclobutenes C-F and corresponding 29 Si NMR of the Si-M silicon nucleus.

  monophosphine-ligated 1,2-palladasilacyclobutene key intermediate in the palladium-catalyzed annulation reaction. With the objective of detecting intermediates derived from the palladacycle, the reaction between 6-PPh 3 and phenyl acetylene was monitored by NMR, at low temperature, in CD 2 Cl 2 . At 203 K, the 1 H NMR revealed the presence of resonance signals attributed to the model silole 5 33 together with the remaining 6-PPh 3 in a 1.33:1 ratio. Besides remaining 6-PPh 3 , the 31 P NMR spectra exhibit an AB spin system with a coupling constant ( 2 J PP = 18.4 Hz) that is indicative of the presence of a Pd complex with two phosphines in the cis position. Based on multinuclear NMR 2D experiments (HMQC 1 H-31 P, HSQC, and HMBC 1 H-13 C) and after comparison to experimental data available in the literature, 34 the Pd(0)-alkyne π-complex [(Ph 3 P) 2 Pd(η2-PhC�CH)] could be identified unambiguously (see S21-S25). It most likely results from ligand redistribution after the release of the silole ring between residual [Pd(PPh 3 ) n ] and the excess of phenylacetylene present in the medium. This experiment clearly shows that the elementary steps occurring in the catalytic cycle after the formation of the 1,2-palladasilacyclobutene intermediate by OA require only little kinetic energy in the case of phenylacetylene. In that respect, the instability of 1,4-palladasilacyclohexadiene (type II species, Scheme 3) toward reductive elimination and silole formation is reminiscent of the behavior of nickel analogues, 25d contrary to the platinum ones that proved stable at room temperature. 25b Theoretical Insight into the Reaction Mechanism. In order to get more insights into the reaction mechanism, DFT calculations at the B3PW91 level of theory have been performed, and the corresponding enthalpy profile computed f7 with silirene 1 and phenylacetylene is shown in Figure 7 (for the Gibbs free energy profile, see Figure S127 in the Supporting Information).

Figure 6 .

 6 Figure 6. X-ray structure of 6-P(OPh) 3 with a displacement ellipsoid plot at a 50% probability level; hydrogen atoms are omitted for clarity. Selected bond lengths [Å] and angles [°]: Pd1-C21 2.085(2), Pd1-Si1 2.3380(7), Pd1-S1 2.6384(7), Pd1-P1 2.2548(7), Si1-C22 1.857(2), C22-C21 1.362(3); P1-Pd1-S1 104.38(2), C21-Pd1-S1 100.47(7), C21-Pd1-Si1 64.42(7), P1-Pd1-Si1 104.38(2), C21-C22-Si1 93.41(16).

Figure 7 .

 7 Figure 7. DFT-computed enthalpy profile (kcal mol -1 ) of the Pd-catalyzed annulation reaction of phenylacetylene with silirene 1; optimized structure of C and bond lengths within the Si-C-C-Pd metallacycle.

Figure 8 .

 8 Figure 8. DFT-computed enthalpy and Gibbs free energy values for the PPh 3 dissociation reaction starting from the Pd(PPh 3 ) 4 compound. The ΔH and ΔG values refer to the starting Pd(PPh 3 ) 4 compound.

  392

408

  Alkyne Insertion Process. From C (6-PPh 3 ), the alkyne 409 may then insert into the Pd-Si bond of the metallacycle to 410 form the 1,4-palladasilacyclohexadiene species. The process occurs in three steps, involving(i) the coordination of the alkyne to C; (ii) the insertion of the alkyne into the Pd-Si bond with the retention of the Pd-Si bond (TS-DE), and (iii) the breaking of the Pd-Si bond (TS-EF) to afford the 1,4palladasilacyclohexadiene species F. As shown in Figure 7, after the formation of D 38 via the endothermic coordination of the alkyne to C in a η 2 side-on fashion (7.1 kcal mol -1 with respect to the entrance channel A), the two-step insertion of the alkyne into the Pd-Si bond takes place through two kinetically accessible barriers of 9.8 and 18.8 kcal mol -1 . The alternative scenario involving the alkyne insertion into the Pd-C bond has been excluded due to its unreasonably high kinetic barrier of 39.6 kcal mol -1 (see Figures S132 and S133 in the Supporting Information). The resulting 1,4-palladasilacyclohexadiene species F is exothermic by -18.2 kcal mol -1 and represents the driving force of the reaction. Two regioisomers are possible depending on whether the terminal alkyne carbon binds the Si atom (Figure 7) or the Pd atom (see Figures S134 and S135 in Supporting Information). As shown in Figures 7 and S134 in the Supporting Information, the formation of the regio-isomer displaying the terminal alkyne carbon bonded to the Si atom is more favorable, both kinetically and thermodynamically (TS-DE = 9.8 kcal mol -1 ; TS-EF = 18.8 kcal mol -1 ; F = -18.2 kcal mol -1 vs TS-D1E1 = 28.6 kcal mol -1 ; TS-E1F1 = 32.3 kcal mol -1 ; F1 = -12.4 kcal mol -1 ); the steric repulsion between the tBu groups at the Si center and the phenyl substituent of the alkyne plays an important role.

  Figures S136 and S137 in the Supporting Information for the

Figure 9 .

 9 Figure 9. Geometry, NPA charge analysis, and representative MOs of the 1,2-metallasilacyclobutene C (isodensity value = 0.05 au).

  intermediates is too small to allow a clear differentiation by means of DFT calculations. To further confirm the presence of one phosphine coordinated to the Pd center during the alkyne insertion process, we computed the phosphine dissociation from adduct D (D4) and the following PhCCH insertion reaction without the PPh 3 coordination. As shown in the enthalpy profile in Figure S143, while the phosphine dissociation from D to D4 is athermic, the following PhCCH insertion becomes kinetically unfavorable, with the corresponding transition state enthalpy passing from 9.8 kcal mol -1 (TS-DE) to 36.4 kcal mol -1 (TS-D4E4). To get more insight into the steric factors, we also computed the annulation reaction of tert-butylacetylene (see Figures S144 and S145 in the Supporting Information). This time the energy of intermediate D_tBu is significantly higher than that of intermediate D (11.3 vs 7.1 kcal mol -1 ), and the formation of the 1,4-palladasilacyclohexadiene compound involves higher kinetic barriers (14.2 and 23.6 kcal mol -1 for TS-DE_tBu and TS-EF_tBu vs 9.8 and 18.8 kcal mol -1 for TS-DE and TS-EF). Compared to phenylacetylene, therefore, the more forcing conditions needed with tert-butylacetylene, (100 °C, 72 h., cat. loading 10 mol %) may be accounted for by both its more endothermic coordination and its less kinetically favorable insertion barriers. ■ CONCLUSIONS In summary, this work contributes to a better understanding of the Pd-catalyzed silole synthesis from alkynes and silacyclopropenes in terms of scope, limitations, and mechanism. The influence of the alkyne substrate was first evaluated, revealing that the reaction is nicely functional group tolerant. When conducted under an acetylene atmosphere, no scrambling of the reagents is observed, with the corresponding silole being the sole product of the reaction. The steric and electronic environment at the alkyne group has a significant impact on the reaction kinetics: while electron-rich alkynes react faster, sterically hindered alkynes display considerably lower reaction rates. In particular, a slow reaction is observed with the bulky tBu-C�C-H whereas no reaction is detected in the case of Me 3 Si-C�C-H. From a mechanistic point of view, the question of the involvement in the process of the hitherto unknown 1,2-palladasilacyclobutene intermediate was duly addressed. Four monophosphine-coordinated 6-PR 3 (R = oTol, tBu, Ph, and OPh) and one cyclooctadiene-ligated 6-COD 1,2-palladasilacyclobutene could be prepared and characterized by multinuclear NMR spectroscopy. The OA of Pd(0) precursors to silacyclopropene 1 was found to be equilibrated in all cases. The nature of the 1,2-palladasilacyclobutenes could be ultimately ascertained by the X-ray diffraction analysis of 6-P(OPh) 3 and 6-COD. Under stoichiometric conditions, 6-PPh 3 reacts cleanly with phenylacetylene to afford the expected silole, thus strongly supporting the involvement of 1,2-palladasilacyclobutene as a key intermediate of the catalytic cycle. The putative 1,4palladasilacyclohexadiene intermediate resulting from the alkyne insertion could not be observed, most likely due to fast reductive elimination and silole formation even at low temperatures. For greater rationalization, we carried out a DFT study on the involved reaction mechanism by computing different possible mechanistic pathways and comparing the theoretical data with the experimental ones. The computational results indicate that the Pd-catalyzed annulation reaction of alkynes with silirene 1 occurs in three main steps, involving

Figure 10 .

 10 Figure 10. DFT-computed enthalpy profiles for the formation of 1,4palladasilacyclohexadiene F from π-complex D with the two possible antiparallel orientation of the thienyl groups.

  , 29.1 (s, CH 3 tBu), 29.3 (s, CH 3 tBu), 40.7 (s, OCH 3 ), 111.6 (s, CH Ph), 126.0 (s, CH vinyl), 127.2 (s, CH Th), 128.1 (s, CH Th), 128.7 (s, CH Ph), 130.0 (s, C Th), 130.5 (s, C Ph), 133.1, 134.1, 135.1, 137.4 and 138.1 (s, C-Th), 140.2 (s, C-Si), 150.0 (s, C Ph), 151.4 (s, Si-CH = C-Ph), 163.1 (s, Si-C=C-Th); 29 Si NMR (CD 2 Cl 2 , 79 MHz): δ (ppm) 16.7 (s). H R M S ( E S I , C H 3 O H / C H 2 C l 2 ( 9 5 / 5 ) ) : c a l c d f o r [C 31 H 40 SiOS 2 +H] + , 521.23626; found, 521.2361 (0 ppm). Anal. Calcd for [C 31 H 40 SiOS 2 ]; C, 71.48; H, 7.74. Found: C, 71.36; H, 7.82. Compound 4b (R = COMe). After flash chromatography on silica gel [SiO 2 , 15 g; gradient DCM/pentane (50/50) → DCM/pentane (70/30); R f (DCM/pentane (50/50)) = 0.60], the product was obtained a clear yellow oil. 1 H NMR (CD 2 Cl 2 , 400 MHz): δ (ppm) 1.10 (s, 9H, CH 3 tBu), 1.16 (s, 9H, CH 3 tBu), 1.73 (s, 3H, CH 3 -Th), 1.79 (s, 3H, CH 3 -Th), 2.16 (s, 3H, CH 3 -Th), 2.39 (s, 3H, CH 3 -Th), 2.53 (s, 3H, C(O)CH 3 ), 5.91 (s, 1H, H Th), 6.24 (s with 29 Si sat., 1H, 2 J HSi = 10.7 Hz, H vinyl), 6.65 (s, 1H, H Th), 7.11 (d, 2H, 3 J HH = 8.5 Hz, H Ph), 7.73 (d, 2H, 3 J HH = 8.5 Hz, H Ph); 13 C{ 1 H} NMR (CD 2 Cl 2 , 100 MHz): δ (ppm) 14.5 (s, CH 3 -Th), 14.6 (s, CH 3 -Th), 15.1 (s, CH 3 -Th), 15.4 (s, CH 3 -Th), 20.0 (s, C tBu), 20.1 (s, C tBu), 26.8 (s, C(O)CH 3 ), 29.0 (s, CH 3 tBu), 29.3 (s, CH 3 tBu), 127.0 (s, CH Th), 127.6 (s, CH Th), 127.7 (s, CH Ph), 128.0 (s, CH Ph), 130.3 (s, C

  130.1 (s, C Th), 133.2 (s, C Th), 134.3 (s, C Th), 134.9 (s, C Ph), 135.2 (s, C Th), 137.1 (s, C Th), 138.0 (s, C Th), 140.3 (s, C-Si), 151.0 (s, Si-CH = C-Ph), 159.0 (s, C Ph), 162.8 (s, Si-C=C-Th); 29 Si NMR (CD 2 Cl 2 , 79 MHz): δ (ppm) 16.9 (s). HRMS (ESI, CH 3 OH/CH 2 Cl 2 (5/95)): calcd for [C 32 H 43 SiNS 2 +H] + , 534.2679; found, 534.2679 (0 ppm).Compound 5-D. The synthesis and purification of 5-D were transposed from the described procedure for the protio-derivative 5 with Ph-C�C-D as a starting material instead of Ph-C�C-H.

17

 17

  1 H NMR (CD 2 Cl 2 , 400 MHz): δ (ppm) 1.10 (s, 9H, CH 3 -tBu), 1.16 (s, 9H, CH 3 -tBu), 1.73 (s, 3H, CH 3 -Th), 1.78 (s, 3H, CH 3 -Th), 2.16 (s, 3H, CH 3 -Th), 2.39 (s, 3H, CH 3 -Th), 5.90 (s, 1H, H-Th),6.65 (s, 1H, H-Th), 6.99-7.05 (m, 2H, H-mPh), 7.11-7.19 (m, 3H, H-oPh and H-pPh); 13 C{ 1 H} NMR (CD 2 Cl 2 , 100 MHz): δ (ppm) 14.5 (s, CH 3 -Th), 14.6 (s, CH 3 -Th), 15.1 (s, CH 3 -Th), 15.4 (s, CH 3 -Th), 19.9 (s, C-tBu), 20.1 (s, C-tBu), 29.0 (s, CH 3 -tBu), 29.3 (s, CH 3 -tBu), 127.0 (s, CH-pPh), 127.1 (s, CH-Th), 127.6 (s, CH-oPh), 127.9 (s, CH-Th), 127.9 (s, CH-mPh), 128.9 (triplet 111, 1 J CD= 22.6 Hz, CD-vinyl), 130.1 (s, C2-Th), 133.3 (s, C2-Th), 134.4 (s, C5-Th), 135.3 (s, C5-Th), 137.0 (s, C3-Th), 137.9 (s, C3-Th), 140.3 (s, C-Si), 142.3 (s, C-ipsoPh), 150.8 (s, Si-C=C-Th), 163.3 (s, Si-CH=C-Ph). HRMS (ASAP, 90 °C): exact mass (monoisotopic) calcd for [C 30 H 37 DSiS 2 ] + , 491.22415; found, 491.2246 (1 ppm). Compound 6-PtBu 3 . A solution of silirene 1 (20.0 mg, 5.15 × 10 -2 mmol) and bis(tri-tert-butylphosphine)palladium(0) (26.2 mg, 5.15 × 10 -2 mmol, 1.00 equiv) in C 6 D 6 (0.65 mL) was heated for 13 h at 50 °C, giving yellow solution. 6-PtBu 3 was characterized in situ by multinuclear NMR spectroscopy, showing a conversion toward the OA product of 40%. 1 H NMR (C 6 D 6 , 400 MHz): δ (ppm) 1.26 (d, 27H, 3 J HP = 11.7 Hz, CH 3 PtBu), 1.53 (s, 18H, CH 3 SitBu), 2.14 (s, 3H, CH 3 -Th), 2.17 (s, 3H, CH 3 -Th), 2.19 (s, 3H, CH 3 -Th), 2.29 (s, 3H, CH 3 -Th), 6.55 (s, 1H, H Th), 6.94 (s, 1H, H Th); 13 C{ 1 H} NMR (C 6 D 6 , 100 MHz): δ (ppm) 14.5 (s, CH 3 -Th), 15.0 (s, CH 3 -Th), 15.3 (s, CH 3 -Th), 15.4 (s, CH 3 -Th), 24.5 (s, C SitBu), 31.8 (s, CH 3 SitBu), 32.8 (d, 2 J CP = 8.3 Hz, CH 3 PtBu), 37.3 (s, C PtBu), 127.3 (s, CH Th), 127.7 (s, CH Th), 128.4 (s, Cquat.), 128.9 (s, Cquat.), 134.3 (s, Cquat.), 134.6 (s, Cquat.),134.8 (d, J CP = 2.4 Hz, Cquat.), 138.7 (d, J CP = 8.8 Hz, Cquat.), 146.4 (d, J CP = 12.8 Hz, Si-C=C), 151.5 (d, J CP = 46.1 Hz, Si-C=C); 29 Si{ 1 H} NMR (C 6 D 6 , 79 MHz): δ (ppm) -33.7 (s); 31 P{ 1 H} NMR (C 6 D 6 , 202 MHz): δ (ppm) 80.9 (s).

Compound 6 -

 6 P(otol) 3 . A solution of silirene 1 (20.0 mg, 5.15 × 10 -2 mmol) and bis(tri-o-tolyl-phosphine)palladium(0)(36.8 mg, 5.15 × 10 -2 mmol, 1.00 equiv) in C 6 D 6 (0.65 mL) was heated for 4 h at 50 °C, giving yellow solution. 6-P(otol) 3 was characterized in situ by multinuclear NMR spectroscopy, showing a conversion toward the OA product of 82%. 1 H NMR (C 6 D 6 , 400 MHz): δ (ppm) 1.35 (s, 18H, CH 3 SitBu), 1.97 (s, 3H, CH 3 -Th), 2.15 (s, 3H, CH 3 -Th), 2.18 (s, 3H, CH 3 -Th), 2.25 (s, 3H, CH 3 -Th), 2.46 (s, 3H, CH 3 tolyl), 6.56 (s, 1H, H Th), 6.96 (s, 1H, H Th); the ortho tolyl proton resonance signals could not be unambiguously assigned due to the complexity of the aromatic region of the spectrum; 13 C{ 1 H} NMR (C 6 D 6 , 100 MHz): δ (ppm) 14.7 (s, CH 3 -Th), 15.1 (s, CH 3 -Th), 15.1 (s, CH 3 -Th), 15.3 (s, CH 3 -Th), 23.7 (d, 3 J CP = 11.8 Hz, CH 3 tolyl), 24.7 (s, C SitBu), 31.3 (s, CH 3 SitBu), 126.0 (d, J CP = 8.9 Hz, CH tolyl), 127.0 (s, CH Th), 127.5 (s, CH Th), 128.5 (s br., Cquat.), 129.3 (s, Cquat.), 130.0 (d, 1 J CP = 28.7 Hz, P-C), 130.4 (s br., CH tolyl), 131.9 (d, 3 J CP = 5.8 Hz, CH tolyl), 134.8 (s, Cquat.), 134.9 (s, Cquat.), 135.5 (d, J CP = 14.7 Hz, CH tolyl), 137.8 (d, J CP = 1.2 Hz, Cquat.), 138.7 (d, J CP = 9.3 Hz, Cquat.), 142.9 (d, J CP = 11.6 Hz, C otolyl), 146.0 (d, J CP = 11.7 Hz, Si-C=C), 152.6 (d, J CP = 55.0 Hz, Si-C=C); 29 Si{ 1 H} NMR (C 6 D 6 , 79 MHz): δ (ppm) -31.6 (s); 31 P{ 1 H} NMR (C 6 D 6 , 162 MHz): δ (ppm) 25.3 (s).

Compound 6 -

 6 PPh 3 . To a solution of silirene 1 (40.0 mg, 0.103 mmol) and triphenylphosphine (27.0 mg, 0.103 mmol, 1.00 equiv) in T H F ( 2 . 5 m L ) w a s a d d e d ( 1 , 5 -c y c l o o c t a d i e n e ) b i s -(trimethylsilylmethyl)palladium(II) (40.1 mg, 0.103 mmol, 1.00 equiv) as a powder in one portion, and the resulting mixture was stirred for 1 h at room temperature, giving a clear green solution.

  13C{ 1 H} NMR (THF-d 8 , 100 MHz): δ (ppm) 14.7 (s, CH 3 -Th), 14.9 (s, CH 3 -Th), 15.0 (s, CH 3 -Th), 15.1 (s, CH 3 -Th), 24.7 (s, C tBu), 31.1 (s, CH 3 tBu), 127.3 (s, CH Th), 127.6 (s, CH Th), 128.7 (d, J CP = 1.7 Hz, Cquat.), 129.2 (s, Cquat.), 129.2 (d, J CP = 9.6 Hz, CH mPh), 130.6 (d, J CP = 1.4 Hz, CH pPh), 134.7 (s, Cquat.), 135.0 (d, J CP = 15.9 Hz, CH oPh), 135.3 (d, 1 J CP = 31.2 Hz, C-P), 135.4 (s, Cquat.), 137.0 (s br., Cquat.), 138.9 (d, J CP = 9.9 Hz, Cquat.), 146.3 (d, J CP = 11.5 Hz, Si-C=C), 156.0 (d, J CP = 63.4 Hz, Si-C=C); 29 Si{ 1 H} NMR (THF-d 8 , 79 MHz): δ (ppm) -27.2 (d, J SiP = 2.1 Hz); 31 P{ 1 H} NMR (THF-d 8 , 162 MHz): δ (ppm) 24.0 (s, 6-PPh 3 ). NMR Evidence for the Existence of an Equilibrium. In the NOESY 1 H, 1 H NMR spectrum, exchange correlations are observed between PPh 3 aromatic proton resonance signals of 6-PPh 3 and [Pd(PPh 3 ) n ]; exchange correlations are observed between thienyl -CH 3 protons resonance signals of 6-PPh 3 and 6-PPh 3 . In the NOESY 31 P, 31 P NMR spectrum, exchange correlations are observed between the PPh 3 resonance signal of 6-PPh 3 and [Pd(PPh 3 ) n ].

902

  The compound 6-P(OPh) 3 was characterized by NMR at -40 °C 903 in THF 8 d. In the case of 29 Si NMR, no resonance signal could be 904 observed at low temperature, and the spectrum was recorded at room 905 temperature over a few hours, resulting in the partial decomposition 906 of the compound due to its thermal instability. Fast 1 H and 31 P NMR 907 spectra could also be recorded at room temperature in CD 2 Cl 2 for a 908 better resolution (see S101-104).

909 1 H

 1 NMR (THF-d 8 , 400 MHz, 233 K): δ (ppm) 1.30 (s, 18H, CH 3 910 tBu), 1.68 (s, 3H, CH 3 -Th), 1.75 (s, 3H, CH 3 -Th), 2.19 (s, 3H, 911 CH 3 -Th), 2.34 (s, 3H, CH 3 -Th), 6.08 (s, 1H, H Th), 6.55 (s, 1H, H 912 Th), 7.

  , 143.5 (s, C-Si),154.6 (s, Cquat.), 171.4 (s, Cquat.).29 Si NMR (C 6 D 6 , 79 MHz): δ (ppm) 13.3 (s). HRMS (ESI, CH 3 OH/CH 2 Cl 2 (90/10)) calcd for [C 28 H 42 SiS 2 +Na] + , 493.23894; found, 493.2394 (1 ppm); calcd for [C 28 H 42 SiS 2 +K] + , 509.21288; found, 509.2130 (0 ppm). Anal. Calcd for [C 28 H 42 SiS 2 ]; C, 71.42; H, 8.99; S, 13.62. Found: C, 71.60; H, 9.00; S, 13.16.

  H NMR (CD 2 Cl 2 , 500 MHz, 243 K): δ (ppm) 1.07 (s, 18H, CH 3 SitBu), 1.96 (s, 3H, CH 3 -Th), 1.98 (s, 3H, CH 3 -Th), 2.06-2.20 (br., 2H, CH 2 COD), 2.20-2.40 (br., 4H, CH 2 COD), 2.29 (s, 3H, CH 3 -Th), 2.30 (s, 3H, CH 3 -Th), 2.52-2.66 (br., 2H, CH 2 COD), 4.88-5.67 (br., 2H, CH COD), 5.90 (br., 2H, CH COD), 6.27 (s, 1H, H Th), 6.43 (s, 1H, H Th); 13 C{ 1 H} NMR (CD 2 Cl 2 , 125 MHz, 243 K): δ (ppm) 14.0 (s, CH 3 -Th), 14.7 (s, CH 3 -Th), 15.2 (s, CH 3 -Th), 15.2 (s, CH 3 -Th), 23.3 (s br., C SitBu), 27.4 (br., CH 2 COD), 30.0 (br., CH 2 COD), 30.7 (s, CH 3 SitBu), 105.1 (s. br., CH COD), 122.2 (s. br., CH COD), 123.2 (s, Cquat.), 125.4 (s, CH Th),126.4 (s, Cquat.), 126.5 (s, CH Th), 133.8, 133.8, 140.8, 146.4, 151.6 and 161.3 (s, Cquat.); 29 Si{ 1 H} NMR (CD 2 Cl 2 , 99 MHz, 243 K): δ (ppm) -34.8 (s).

1

https://doi.org/10.1021/acs.inorgchem.3c00045Inorg. Chem. XXXX, XXX, XXX-XXX

https://doi.org/10.1021/acs.inorgchem.3c00045Inorg. Chem. XXXX, XXX, XXX-XXX J