Efficient click-addition sequence for polymer–polymer couplings

Controlled radical polymerization methods and click chemistry form a versatile toolbox for creating complex polymer architectures. However, the incompatibility between the functional groups required for click reactions and the reaction conditions of radical polymerization techniques often limits application. Here, we demonstrate how combining two complementary click reactions in a sequence circumvents compatibility issues. We employ isocyanate-amine addition on a polymer obtained by RAFT without purification, thus allowing us to work at exact equimolarity. The addition of commercially available amine-functional azido or strained alkyne compounds, yields orthogonally modified polymers, which can be coupled together in a subsequent strain promoted cycloaddition (SPAAC). The efficiency of this reaction sequence is demonstrated with different acrylate, methacrylate, and acrylamide polymers giving block copolymers in high yield. The resulting diblock copolymers remain active towards RAFT polymerization, thus allowing access to multiblock structures by simple chain extension. The orthogonality of the isocyanate-amine reaction, SPAAC and RAFT polymerization (both in terms of monomer and chain end groups) is a key advantage and offers access to functional and challenging polymer architectures without the need for stringent reaction conditions or laborious intermediate purifications

An LLVM Instrumentation Plug-in for Score-P

Reducing application runtime, scaling parallel applications to higher numbers of processes/threads, and porting applications to new hardware architectures are tasks necessary in the software development process. Therefore, developers have to investigate and understand application runtime behavior. Tools such as monitoring infrastructures that capture performance relevant data during application execution assist in this task. The measured data forms the basis for identifying bottlenecks and optimizing the code. Monitoring infrastructures need mechanisms to record application activities in order to conduct measurements. Automatic instrumentation of the source code is the preferred method in most application scenarios. We introduce a plug-in for the LLVM infrastructure that enables automatic source code instrumentation at compile-time. In contrast to available instrumentation mechanisms in LLVM/Clang, our plug-in can selectively include/exclude individual application functions. This enables developers to fine-tune the measurement to the required level of detail while avoiding large runtime overheads due to excessive instrumentation.Comment: 8 page

Poly(bromoethyl acrylate) : a reactive precursor for the synthesis of functional RAFT materials

Postpolymerization modification has become a powerful tool to create a diversity of functional materials. However, simple nucleophilic substitution reactions on halogenated monomers remains relatively unexplored. Here we report the synthesis of poly(bromoethyl acrylate) (pBEA) by reversible addition–fragmentation chain transfer (RAFT) polymerization to generate a highly reactive polymer precursor for postpolymerization nucleophilic substitution. RAFT polymerization of BEA generated well-defined homopolymers and block copolymers over a range of molecular weights. The alkylbromine-containing homopolymer and block copolymer precursors were readily substituted by a range of nucleophiles in good to excellent conversion under mild and efficient reaction conditions without the need of additional catalysts. The broad range of nucleophilic species that are compatible with this postmodification strategy enables facile synthesis of complex functionalities, from permanently charged polyanions to hydrophobic polythioethers to glycopolymers

MEET YOUR NEW COLLE(AI)GUE – EXPLORING THE IMPACT OF HUMAN-AI INTERACTION DESIGNS ON USER PERFORMANCE

Artificial Intelligence (AI) has an increasing impact on industries, establishing a new way of solving tasks and automating work routines. While AI-based systems have become new colleagues for some processes, the tasks of some humans have shifted towards supervising AI. Essentially, humans need to adapt to a new form of interaction with AI-based systems because AI functioning is more similar to cognitive processes of humans than traditional information systems, e.g., in terms of their intransparent decision making. Previous research indicates that AI adds new challenges to human-computer interaction, and new frameworks for human-AI interaction are developed. However, current research lacks empirical research on the design of such interactions. We conducted a 2x2x2 experiment of AI-supported information extraction and measured the ability of participants to validate the extracted information by the AI. Our results indicate that the design of human-AI interaction significantly impacts users’ supervising performance

Is Making Mistakes Human? On the Perception of Typing Errors in Chatbot Communication

The increasing application of Conversational Agents (CAs) changes the way customers and businesses interact during a service encounter. Research has shown that CA equipped with social cues (e.g., having a name, greeting users) stimulates the user to perceive the interaction as human-like, which can positively influence the overall experience. Specifically, social cues have shown to lead to increased customer satisfaction, perceived service quality, and trustworthiness in service encounters. However, many CAs are discontinued because of their limited conversational ability, which can lead to customer dissatisfaction. Nevertheless, making errors and mistakes can also be seen as a human characteristic (e.g., typing errors). Existing research on human-computer interfaces lacks in the area of CAs producing human-like errors and their perception in a service encounter situation. Therefore, we conducted a 2x2 online experiment with 228 participants on how CAs typing errors and CAs human-like behavior treatments influence user’s perception, including perceived service quality

Is it COVID or a Cold? An Investigation of the Role of Social Presence, Trust, and Persuasiveness for Users\u27 Intention to Comply with COVID-19 Chatbots

The COVID-19 pandemic challenged the existing healthcare system by demanding potential patients to self-diagnose and self-test a potential virus contraction. In this process, some individuals need help and guidance. However, the previous modus-operandi to go to a physician is no longer viable because of the limited capacity and danger of spreading the virus. Hence, digital means had to be developed to help and inform individuals at home, such as conversational agents (CA). The human-like design and perceived social presence of such a CA are central to attaining users’ compliance. Against this background, we surveyed 174 users of a commercial COVID-19 chatbot to investigate the role of perceived social presence. Our results provide support that the perceived social presence of chatbots leads to higher levels of trust, which are a driver of compliance. In contrast, perceived persuasiveness seems to have no significant effect

You are an Idiot! – How Conversational Agent Communication Patterns Influence Frustration and Harassment

Conversational Agents (CA) in the form of digital assistants on smartphones, chatbots on social media, or physical embodied systems are an increasingly often applied new form of user interfaces for digital systems. The human-like design of CAs (e.g., having names, greeting users, and using self-references) leads to users subconsciously reacting to them as they were interacting with a human. In recent research, it has been shown that this social component of interacting with a CA leads to various benefits, such as increased service satisfaction, enjoyment, and trust. However, numerous CAs were discontinued because of inadequate responses to user requests or only making errors because of the limited functionalities and knowledge of a CA, which can lead to frustration. Therefore, investigating the causes of frustration and other related emotions and reactions highly relevant. Against this background, this study investigates via an online experiment with 169 participants how different communication patterns influence user’s perception, frustration, and harassment behavior of an error producing CA