Recently, an epoch-making printing technology called “SuPR-NaP (Surface Photo-Reactive Nanometal Printing)” that allows easy, high-speed, and large-area manufacturing of ultrafine silver wiring patterns has been developed. Here we demonstrate low-voltage operation of organic thin-film transistors (OTFTs) composed of printed source/drain electrodes that are produced by the SuPR-NaP technique. We utilize an ultrathin layer of perfluoropolymer, Cytop, that functions not only as a base layer for producing patterned reactive surface in the SuPR-NaP technique but also as an ultrathin gate dielectric layer of OTFTs. By the use of 22 nm-thick Cytop gate dielectric layer, we successfully operate polycrystalline pentacene OTFTs below 2 V with negligible hysteresis. We also observe the improvement of carrier injection by the surface modification of printed silver electrodes. We discuss that the SuPR-NaP technique allows the production of high-capacitance gate dielectric layers as well as high-resolution printed silver electrodes, which provides promising bases for producing practical active-matrix OTFT backplanes.

Continued advances in digital design software and 3D printing methods enable innovative approaches in the development of new educational tools for laboratory-based STEM (science, technology, engineering and mathematics) learning. The decreasing cost of 3D printing equipment and greater access provided by university fabrication centers afford unique opportunities for educators to transcend the limitations of conventional modes of student engagement with analytical instrumentation. This work shares successful efforts at Wabash College to integrate user-friendly and inexpensive 3D printed instruments kits into introductory STEM coursework. The laboratory kits and activities described provide new tools for engaging students in the exploration of instrument design and performance. These experiences provide effective ways to assist active-learners in discovering the technology and fundamental principles of analysis and deliberately confront the “black box” perception of instrumentation.

A 3D printed device has been developed for cost-effective production of rifampicin loaded lipid polymer hybrid nanoparticles. These nanoparticles show considerable potential for research related to the treatment of Tuberculosis. The nanoparticles synthesized by the device possess a core-shell drug-lipid polymer assembly. The synthesis conditions have been optimized with respect to the parameters like flow-rate, size of device, and the concentration of rifampicin and poly lactic-co-glycolic acid (in which the drug molecules are incorporated). The nanoparticles synthesized by the 3D printed device yield smaller nanoparticles with narrow size distributions in contrast to traditional sonication method. The device can be operated either by hand or by using syringe pumps. These nanoparticles also show excellent antibacterial activity which typically correlates with a reduction in drug dosing frequency to promote patient adherence to drug regimens.

In this work, a printable tungsten disulfide (WS2) based ink is developed from readily available WS2 powder (0.6 µm average particle size), and an ink-jet printing based deposition method for a tungsten disulfide film is presented. WS2 flake coverage and bulk electrical characteristics under three different irradiance conditions are examined and discussed. Presence of excitons in the absorbance of the inks is performed by optical UV-Vis spectrometry. Metrics using the A exciton peak generated by the few-layered flakes are used to calculate the average flake lateral dimensions, the concentration of WS2 in the inks after size selection and filtering, as well as the average monolayer count of the flakes. After printing, scanning electron microscopy is used to confirm average flake lateral size and average flake area coverage, while an atomic force microscope is used to confirm flake thickness.

Mechanical properties similar to natural bone and good biocompatibility make magnesium a great option for its use as biodegradable implant material. The use of castings as fabrication technique brings inherent problems, such as segregation and void formation. Also, the architecture of the specimens created by using these techniques is limited. This study shows the implementation of WAAM for the fabrication of elements made out of AZ91D magnesium alloy (9% aluminium and 1% zinc). The results demonstrate that porosity or cracking only appears at the surface of the individual printed lines, while the central sections presents a void-less structure composed by an HCP magnesium matrix and a high density of well dispersed aluminium-zinc rich precipitates. EDS mapping confirms the presence of orthorhombic Al5Mg11Zn4 phase. Also, the relationship between the heat present in the system and the morphology of the lines is analysed.

Robot assisted synthesis as part of high-throughput (HT) technology can assist in the creation of polymer libraries, e.g. polymers with a variety of molecular weights, by automatizing similar reactions. Especially for multiblock copolymers like polyurethanes (PUs) synthesized from telechels via polyaddition reaction, the adjustment of equivalent molar amounts of reactants requires a comprehensive investigation of end group functionality.

In this work, PUs based on oligo(ε-caprolactone) (OCL) / oligotetrahydrofuran (OTHF) as model components were designed utilizing HT synthesis enabling the quantitative determination of the optimized ratio between reactive end-groups via fully automated syntheses without major characterization effort of end group functionality. The semi-crystalline oligomeric telechelics were connected with a diisocyanate and OCL with a molecular weight of 2, 4, or 8 kg∙mol-1 was integrated. Here, optimized molecular weights between 90 ± 10 kg∙mol-1 (in case of OCL 8 kg∙mol-1) and 260 ± 30 kg∙mol-1 (in case of OCL 2 kg∙mol-1) were obtained with an isocyanate content of 120 mol%, whereby 100 mol% of isocyanate groups resulted only in molecular weights between 60 ± 6 kg∙mol-1 (OCL 8 kg∙mol-1) and 80 ± 10 kg∙mol-1 (OCL 2 kg∙mol-1). In addition to the optimized ratio between isocyanate and hydroxy end groups, quantitative influences of the OCL chain length and overall molecular weights of PUs on thermal and mechanical properties were detected. The melting temperatures (Tms) of OCL and OTHF domains were well separated for PUs of low molecular weight, the temperature interval between the Tms decreased when the molecular weight of the PUs was increased, and were even overlapping towards one broad Tm, when OCL 2 kg∙mol-1 was incorporated. The storage modulus E’ was highly dependent on OCL chain length exhibiting an increase with increasing molecular weight of OCL from 220 MPa to 440 MPa at 0 °C and decreased with increasing chain length of PUs. The elongation at break (εb) was analyzed below and above Tm of OTHF resulting in εb = 780-870% at 0 °C and εb = 510-830% at 30 °C for PUs of high molecular weight. Accordingly, stretchability of PUs was almost independent of the state of OTHF (semi crystalline or amorphous) but correlated with the OCL precursor chain length (increasing εb with increasing chain length) and overall molecular weight of PUs (PUs at higher molecular weight exhibited higher εb). Hence, the analysis of these quantitative influences between macromolecular structure of multiblock copolymers and the resulting properties (well separated Tms versus overlapping melting transition, improvement of stretchability) would enable the design of new tailored PUs.

We developed a new type of polymer descriptor based on Extended Connectivity Fingerprints. The number densities, that are substructure numbers divided by the number of atoms in a polymer model, were employed. We found that this approach is superior in accurately predicting linear polymer properties, compared to the conventional approach, where just the substructure numbers are used as descriptors. In addition, dimension reduction and multiple replication of repeat unit were found to improve prediction accuracy. As a result, the novel descriptor based on the Extended Connectivity Fingerprints with machine learning approaches was found to achieve accurate prediction of the refractive indices of linear polymers, which is comparable to that by ab initio density functional theory. Although process-dependent properties such as mechanical properties were difficult to predict, the present approach was found to be applicable to prediction of substructure-dependent properties, for example, optical properties, thermal stabilities.