Bridging the Molecular/Material Divide: An Investigation into the Properties of Polyesters

By definition, polymers consist of large molecules composed of small, repeating units called monomers. These monomers give characteristic material properties to polymers, whether they be the melting point, the folding character, or the inherent stability. Many of the objects we encounter on a daily basis are composed of polymers. These polymers may be synthetic, such as the plastic in disposable bags, or natural, like glycogen in the liver. Recent developments in biotechnology have used synthetic polymers in drug delivery, contact lenses, and even organ transplants. Polyesters, in particular, have been utilized pharmacologically due to their biodegradable properties. Understandably, not all polymers are the same, nor can the properties of a polymer be deduced from the molecular properties of the individual monomer. A quick literature review can give you the melting point of an individual monomer and that of a polymer of one hundred repeat units, but nothing in-between. At what length do polymers start to exhibit material properties rather than molecular properties? Can the initiators used during polymerization affect these material properties? If so, when do the differences in the initiator effects become negligible? Through organocatalytic ring-opening polymerization of polyesters of various lengths and subsequent characterization, this research aims to answer these three fundamental questions

Deep learning computer vision for robotic disassembly and servicing applications

Fastener detection is a necessary step for computer vision (CV) based robotic disassembly and servicing applications. Deep learning (DL) provides a robust approach for creating CV models capable of generalizing to diverse visual environments. Such DL CV systems rely on tuning input resolution and mini-batch size parameters to fit the needs of the detection application. This paper provides a method for determining the optimal compromise between input resolution and mini-batch size to determine the highest performance for cross-recessed screw (CRS) detection while utilizing maximum graphics processing unit resources. The Tiny-You Only Look Once v2 (Tiny-YOLO v2) DL object detection system was chosen to evaluate this method. Tiny-YOLO v2 was employed to solve the specialized task of detecting CRS which are highly common in electronic devices. The method used in this paper for CRS detection is meant to lay the ground-work for multi-class fastener detection, as the method is not dependent on the type or number of object classes. An original dataset of 900 images of 12.3 MPx resolution was manually collected and annotated for training. Three additional distinct datasets of 90 images each were manually collected and annotated for testing. It was found an input resolution of 1664 x 1664 pixels paired with a mini-batch size of 16 yielded the highest average precision (AP) among the seven models tested for all three testing datasets. This model scored an AP of 92.60% on the first testing dataset, 99.20% on the second testing dataset, and 98.39% on the third testing dataset

Screening the PRISM Library against Staphylococcus aureus Reveals a Sesquiterpene Lactone from Liriodendron tulipifera with Inhibitory Activity

Infections caused by the bacterium Staphylococcus aureus continue to pose threats to human health and put a financial burden on the healthcare system. The overuse of antibiotics has contributed to mutations leading to the emergence of methicillin-resistant S. aureus, and there is a critical need for the discovery and development of new antibiotics to evade drug-resistant bacteria. Medicinal plants have shown promise as sources of new small-molecule therapeutics with potential uses against pathogenic infections. The principal Rhode Island secondary metabolite (PRISM) library is a botanical extract library generated from specimens in the URI Youngken Medicinal Garden by upper-division undergraduate students. PRISM extracts were screened for activity against strains of methicillin-susceptible S. aureus (MSSA). An extract generated from the tulip tree (Liriodendron tulipifera) demonstrated growth inhibition against MSSA, and a bioassay-guided approach identified a sesquiterpene lactone, laurenobiolide, as the active constituent. Intriguingly, its isomers, tulipinolide and epi-tulipinolide, lacked potent activity against MSSA. Laurenobiolide also proved to be more potent against MSSA than the structurally similar sesquiterpene lactones, costunolide and dehydrocostus lactone. Laurenobiolide was the most abundant in the twig bark of the tulip tree, supporting the twig bark’s historical and cultural usage in poultices and teas

Modeling of the backlash hysteresis nonlinearity

In this paper, the backlash hysteresis nonlinearity is examined, and a mathematical model based on the particular behavior of the backlash is developed. A geometrical interpretation of this model using switching operators is provided to facilitate an understanding of it. Simulation of output prediction of the backlash mechanism using an arbitrary input signal is performed to verify the correctness of the developed model. A linearization scheme based on the model is also developed to compensate for the backlash nonlinearity. The scheme was applied for tracking an arbitrary signal similar to that used in the modeling. The results show that our model-based linearization scheme although running in an open loop fashion, provides a simpler way to perform tracking control for structures with backlash hysteresis nonlinearity. © 1998 by ASME

Investigation of surface roughness characterization using an ART2 neural network

Surface roughness is an important quality of surfaces. A shortcoming of currently used techniques for characterizing surface roughness is the absence of a single descriptive number that can be used to differentiate the roughness of different surfaces. This paper discusses the development of an Adaptive Resonance Theory (ART2) neural network-based approach for surface roughness analysis. The objective is to develop a unique, more descriptive parameter of surface roughness. ART2 neural networks offer an attractive approach to classify sensory data such as surface roughness profiles, since they have the capability of classifying the data in a self-scaling, unsupervised fashion, and of deciphering the data\u27s invariant properties. In this paper, an ART2 network structure is enhanced to enable the network to address the need of attributing a single numerical value to each classified category. The above methodology was tested on six simulated surfaces and the results obtained showed that the ART2 network is capable of classifying each surface into a distinct category and assigning a roughness index to each category that is more descriptive than the commonly used CLA parameter. However, further work is still needed to obtain the ultimate single number index

Modeling and control of a micro-positioning tower

The design of a feedback controller for the z-motion of a positioning tower made up of a vertical motion micro-positioning stage situated on a pair of x-y Klinger stages is discussed. A three-degree of freedom dynamic model was built using frequency response experimental data to represent the dynamics of the micro-positioning tower system in the vertical direction. Based on this model, a controller was designed which combines a PID closed-loop controller and a 4th-order Chebyshev lowpass filter in series. Both simulation and real-time tests were performed for the designed control scheme, and the tracking control performance, disturbance rejection and oscillation suppression have improved using this controller. The settling time of the designed closed-loop control system is one third of that of the open-loop system, and frequency response tests on the real system show that a flat magnitude response with less than 3 dB attenuation is achieved over the frequency range of 0-75 Hz using this controller. © 1997 Elsevier Science Ltd

Design and characterization of a precision fluid dispensing valve

Precision fluid dispensing is a complicated but yet relevant process where fluid is dispensed in a controlled manner. It is important and applicable in an array of applications ranging from dispensing of food products, biomedical applications to dispensing of adhesive and encapsulants in the semi-conductor industry. This paper discusses the design and characterization of a precision fluid dispensing hybrid valve based on the time-pressure, positive displacement pump, and adhesive jetting technologies. The main advantage of this dispensing method is that the amount of fluid dispensed is independent of the standoff height and does not rely on surface tension between the substrate and the fluid for clean dispensation. Specifically, the dispensed fluid is jetted from a fixed needle height, and hence, repeatability and accuracy is improved while eliminating vertical travel. A prototype valve was built and tested for precision and accuracy of milligrams over a range of pressures and time. The test results are promising, indicating high repeatability and accuracy for low to medium viscous materials and are comparable to existing commercially available precision dispensing systems

Using the Soar Cognitive Architecture to Remove Screws from Different Laptop Models

This paper investigates an approach that uses the cognitive architecture Soar to improve the performance of an automated robotic system, which uses a combination of vision and force sensing to remove screws from laptop cases. Soar\u27s long-term memory module, semantic memory, was used to remember pieces of information regarding laptop models and screw holes. The system was trained with multiple laptop models and the method in which Soar was used to facilitate the removal of screws was varied to determine the best performance of the system. In all the cases, Soar could determine the correct laptop model and in what orientation it was placed in the system. Soar was also used to remember what circle locations that were explored contained screws and what circles did not. Remembering the locations of the holes decreased a trial time by over 60%. The system performed the best when the number of training trials used to explore circle locations was limited, as this decreased the total trial time by over 10% for most of the laptop models and orientations. Note to Practitioners - Although the amount of discarded electronic waste in the world is rapidly increasing, efficient methods that can handle this in an automated non-destructive fashion have not been developed. Screws are a common fastener used on electronic products, such as laptops, and must be removed during nondestructive methods. In this paper, we focus on using the cognitive architecture Soar to facilitate the disassembly sequence of removing these screws from the back of laptops. Soar is able to differentiate between different models of laptops and store the locations of screws for these models leading to an improvement of the disassembly time when the same laptop model is used. Currently, this paper only uses one of Soar\u27s long-term memory modules (semantic memory) and a screwdriver tool. However, this paper can be extended to use multiple tools by using different features available in Soar such as other long-term memory modules and substates