Nanoarchitectural Evolution from Laser-Produced Colloidal Solution: Growth of Various Complex Cadmium Hydroxide Architectures from Simple Particles

Complex nanostructures and nanoassemblies have exhibited their potential application in the fabrication of future molecular machines and molecular devices. Liquid phase pulsed laser ablation (LP-PLA) is an easy, versatile, environmentally friendly, and rapidly growing method for the synthesis of nanostructured materials. Several experimental laser and liquid media parameters have been developed, but others are under development. The interaction of an anionic surfactant with the nanomaterials having a positive surface charge density is a key parameter, but an unanswered question until now, in the field of LP-PLA. Nanosecond pulsed laser ablation of a cadmium rod placed on the bottom of a glass vessel containing aqueous media of sodium dodecyl sulfate at different concentrations is used to produce a variety of cadmium hydroxide nanostructures from nanoparticles to nanorods, nanotetrapods, nanoflower buds, and 2D and 3D nanoflowers in order to investigate the above liquid media parameter. It is suggested that initially produced spherical nanoparticles get self-assembled into 1D nanorods, which themselves also get assembled into their successor nanoarchitectures. An aqueous medium of 20 mM SDS is found most suitable for the growth of such nanostructures. An increase of the surfactant concentration induces the synthesis of higher aspect ratio 1D nanorods with a larger tendency of aggregation and agglomeration. The rate of increase of agglomeration and aggregation with the surfactant concentration is so high that the nanomaterials produced in 100 mM surfactant concentration lose their individual identity. A detailed investigation on the evolution, growth, and self-assembly of various nanostructures is presented

Comparing platform owners' early and late entry into complementary markets

Research on platform owners’ entry into complementary markets points in divergent directions. One strand of the literature reports a squeeze on post-entry complementor profits due to increased competition, while another strand observes positive effects as increased customer attention and innovation benefit the complementary market as a whole. In this research note, we seek to transcend these conflicting views by comparing the effects of the early and late timing of platform owners’ entry. We apply a difference-in-differences design to explore the drivers and effects of the timing of platform owners' entry using data from three entries that Amazon made into its Alexa voice assistant’s complementary markets. Our findings suggest that early entry is driven by the motivation to boost the overall value creation of the complementary market, whereas late entry is driven by the motivation to capture value already created in a key complementary market. Importantly, our findings suggest that early entry, contrary to late entry, creates substantial consumer attention that benefits complementors that offer specialized functionality. In addition, they also suggest that complementors with more experience are more likely to benefit from the increased consumer attention. We contribute to platform research by showing that the timing of the platform owner’s entry matters in a way that potentially can reconcile conflicting findings regarding the consequences of platform owners' entry into complementary markets. </p

Comparing platform owners' early and late entry into complementary markets

Research on platform owners’ entry into complementary markets points in divergent directions. One strand of the literature reports a squeeze on post-entry complementor profits due to increased competition, while another strand observes positive effects as increased customer attention and innovation benefit the complementary market as a whole. In this research note, we seek to transcend these conflicting views by comparing the effects of the early and late timing of platform owners’ entry. We apply a difference-in-differences design to explore the drivers and effects of the timing of platform owners' entry using data from three entries that Amazon made into its Alexa voice assistant’s complementary markets. Our findings suggest that early entry is driven by the motivation to boost the overall value creation of the complementary market, whereas late entry is driven by the motivation to capture value already created in a key complementary market. Importantly, our findings suggest that early entry, contrary to late entry, creates substantial consumer attention that benefits complementors that offer specialized functionality. In addition, they also suggest that complementors with more experience are more likely to benefit from the increased consumer attention. We contribute to platform research by showing that the timing of the platform owner’s entry matters in a way that potentially can reconcile conflicting findings regarding the consequences of platform owners' entry into complementary markets. </p

AI-assisted Single-Image Full-Frame Camera Calibration for Space-Constrained Stereoscopic Systems

Camera calibration plays a fundamental role in the wake of the newly emerging image data-driven technologies, where pinpoint accuracy in data is vital to the successful functioning of these systems. Conventional camera calibration algorithms require manual object placement, a process that can be exceptionally time-consuming and labor-intensive, particularly in scenarios where space constraints or delicate equipment are involved. We present an innovative calibration object and AI-aided pre-calibration routine with a specific emphasis on space-restricted environments. The proposed methodology obviates the need for manual multi-image acquisition. This is achieved by fabricating the novel calibration object, which contains 20 checkerboards in different positions and orientations. The precursor routine, assisted by an AI model, isolates and processes the individual checkerboards, which is then used as input for the camera calibration. We report an accuracy of 99.92% for ML-assisted checkerboard separation, with procedure time improved by nearly 64x and overall corrected reprojection error consistently below 0.5 pixels. Incorporating the proposed calibration routine into a 3D vascular imaging stereovision system, we demonstrate a depth resolution of 0.5mm