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Povzetek: The surface properties of hydrocarbon polymers are inadequate for numerous applications. Hence, they require alteration via functionalisation with desired functional groups. Hydroxyl groups are often preferred, since they enable appropriate polarity for the irreversible grafting of desired molecules. In this study, the surface kinetics resulting from the treatment of polystyrene with hydroxyl (OH) radicals from the gas phase was fundamentally investigated through a precisely-designed experiment. Polystyrene samples were exposed to various known fluences of OH radicals, and the evolution of surface functional groups versus the OH fluence was monitored using high-resolution X-ray photoelectron spectroscopy (XPS). The fluences of OH radicals varied between 1 × 1018 and 4 × 1023 m−2 in the process of finding a threshold fluence for the formation of specific groups. The surface concentration of carbonyl (C=O) groups could be measured using XPS at a fluence of approximately 5 × 1020 m−2. The C=O groups became measurable at a fluence of approximately 1.5 × 1021 m−2, and carboxyl (COOH)/ester groups at approximately 4 × 1021 m−2. As deduced from the XPS, a concentration of C=O groups at approximately 5 % occurred before the degradation of the aromatic ring. The formation of other oxygen-functional groups required opening of the aromatic ring. The results have been explained using a two-step process, considering available theories vis-a-vis initial stages in the functionalisation of PS with polar functional groups.
Ključne besede: polistiren, kinetika površinske funkcionalizacije, OH radikali, vpliv doze radikalov, časovni razvoj, polystyrene, surface functionalisation kinetics, OH radicals
Objavljeno v DiRROS: 09.11.2023; Ogledov: 90; Prenosov: 54
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Ključne besede: Ions, Layers, Mass spectrometry, Metals, Oxides, SIMS depth profiling H2 C2H2 CO and O2 atmosphere gas flooding cluster secondary ions matrix effect
Objavljeno v DiRROS: 18.10.2023; Ogledov: 153; Prenosov: 72
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Povzetek: In this study, we estimate the generalization of the performance of previously proposed predictive models for nutrient value prediction across different recipe datasets. For this purpose, we introduce a quantitative indicator that determines the level of generalization of using the developed predictive model for new unseen data not presented in the training process. On a predefined corpus of recipe embeddings from six publicly available recipe datasets (i.e., projecting them in the same meta-feature vector space), we train predictive models on one of the six recipe datasets and test the models on the rest of the datasets. In parallel, we define and calculate generalizability indexes which are numbers that indicate how generalizable a predictive model is i.e., how well will a predictive model learned on one dataset perform on another one not involved in the training. The evaluation results prove the validity of these indexes – their relation with the accuracy of the predictions. Further, we define three sampling techniques for selecting representative data instances that will cover all parts from the feature space uniformly (involving data from all datasets) and further will improve the generalization of a predictive model. We train predictive models with these generalized datasets and test them on instances from the six recipe datasets that are not selected and included in the generalized datasets. The results from the evaluation of these predictive models show improvement compared to the results from the predictive models trained on one recipe dataset and tested on the others separately.
Ključne besede: ML pipeline, predictive modeling, nutrient prediction, recipe datasets
Objavljeno v DiRROS: 25.09.2023; Ogledov: 220; Prenosov: 112
Celotno besedilo (3,27 MB)
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Povzetek: The paper addresses the issue of learning tasks where a robot maintains permanent contact with the environment. We propose a new methodology based on a hierarchical learning scheme coupled with task representation through directed graphs. These graphs are constituted of nodes and branches that correspond to the states and robotic actions, respectively. The upper level of the hierarchy essentially operates as a decision-making algorithm. It leverages reinforcement learning (RL) techniques to facilitate optimal decision-making. The actions are generated by a constraint-space following (CSF) controller that autonomously identifies feasible directions for motion. The controller generates robot motion by adjusting its stiffness in the direction defined by the Frenet–Serret frame, which is aligned with the robot path. The proposed framework was experimentally verified through a series of challenging robotic tasks such as maze learning, door opening, learning to shift the manual car gear, and learning car license plate light assembly by disassembly.
Ključne besede: autonomous robot learning, learning, experience, compliance and impedance contro
Objavljeno v DiRROS: 21.09.2023; Ogledov: 198; Prenosov: 103
Celotno besedilo (1,73 MB)
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Ključne besede: algorithms, predictive models, machine learning
Objavljeno v DiRROS: 15.09.2023; Ogledov: 159; Prenosov: 104
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