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Soliton microcombs have attracted considerable research interest due to their unique properties. Being able to directly access the single-soliton state in a Kerr microresonator simplifies the device operation and may inspire new applications. However, the general conditions leading to such operations are not well understood. In this work, we aim to elucidate the key factors enabling the direct access of the single-soliton state in a Kerr microresonator by combining the experimental results in an integrated silicon carbide platform and a comprehensive analysis based on the normalized Lugiato-Lefever equation. A general criterion linking the Kerr nonlinearity, dispersion, and thermo-optic properties has been derived, which is applicable to Kerr microresonators with varied materials, sizes, optical quality factors, and dispersion. 

Abstract Entanglement plays a vital role in quantum information processing. Owing to its unique material properties, silicon carbide recently emerged as a promising candidate for the scalable implementation of advanced quantum information processing capabilities. To date, however, only entanglement of nuclear spins has been reported in silicon carbide, while an entangled photon source, whether it is based on bulk or chip-scale technologies, has remained elusive. Here, we report the demonstration of an entangled photon source in an integrated silicon carbide platform for the first time. Specifically, strongly correlated photon pairs are efficiently generated at the telecom C-band wavelength through implementing spontaneous four-wave mixing in a compact microring resonator in the 4H-silicon-carbide-on-insulator platform. The maximum coincidence-to-accidental ratio exceeds 600 at a pump power of 0.17 mW, corresponding to a pair generation rate of (9 ± 1) × 10³pairs/s. Energy-time entanglement is created and verified for such signal-idler photon pairs, with the two-photon interference fringes exhibiting a visibility larger than 99%. The heralded single-photon properties are also measured, with the heraldedg⁽²⁾(0) on the order of 10⁻³, demonstrating the SiC platform as a prospective fully integrated, complementary metal-oxide-semiconductor compatible single-photon source for quantum applications. 

Abstract Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO₂Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr⁻¹and pH decrease of 0.0014 ± 0.0004 yr⁻¹. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal. 

Over the past decade, the educational landscape has experienced a surge of online learning and instruc-tional platforms (Liu et al., 2020). This remarkable surge can be attributed to a confluence of factors, including the rising demand for higher education opportunities, the shortage of available teaching staff, and the rapid advancements in information technology and artificial intelligence capabilities. Artificial Intelligence (AI) remained a niche area of research with limited practical applications in education for over half a century (Bhutoria, 2022; Chen et al., 2020; Roll & Wylie, 2016) from 1950 to 2010. Howev-er, in recent years, the advent of Big Data and advancements in computing power have propelled AI into the educational mainstream (Alam, 2021; Chen et al., 2020; Hwang et al., 2020). Today, the rise of machine learning, deep learning, automation, together with advances in big data analysis has sparked novel perspectives and explorations around the potential of enhancing personalized learning, a long-term educational vision of technology-enhanced course options to meet student needs (Grant & Basye, 2014). Fostering personalized learning necessitates the development of digital learning environments that dynamically adapt to individual learners' knowledge, prior experiences, and interests, while effectively and efficiently guiding them towards achieving desired learning outcomes (Spector, 2014, 2016). AI-powered technologies have made it possible to analyze data generated by learners and provide instruc-tion that matches their learning performance. Through learning analytics and data mining techniques, large datasets collected are analyzed and processed to uncover learners' unique learning characteristics, often referred to as learner profiling (Tzouveli et al., 2008). Subsequently, leveraging artificial intelli-gence algorithms, the learning content is tailored, and personalized learning paths are designed to align with each learner's identified needs and preferences, thereby facilitating personalized learning experienc-es. 

The existing silicon-carbide-on-insulator photonic platform utilizes a thin layer of silicon dioxide under silicon carbide (SiC) to provide optical confinement and mode isolation. Here, we replace the underneath silicon dioxide layer with 1-µm-thick aluminum nitride and demonstrate a 4H-silicon-carbide-on-aluminum-nitride integrated photonic platform for the first time to our knowledge. Efficient grating couplers, low-loss waveguides, and compact microring resonators with intrinsic quality factors up to 210,000 are fabricated. In addition, by undercutting the aluminum nitride layer, the intrinsic quality factor of the silicon carbide microring is improved by nearly one order of magnitude (1.8 million). Finally, an optical pump–probe method is developed to measure the thermal conductivity of the aluminum nitride layer, which is estimated to be over 30 times of that of silicon dioxide. 

Dicyanobenzothiadiazole based dyes have shown exceptional tunability of oxidizing strength and absorption energy. The photophysical thermodynamic and kinetic properties are studied with desirable properties shown for future use in photocatalysis. 

RAFT step-growth polymerizationviathe Z-group approach was developed, offering a facile method to prepare deconstructable (multiblock) polymers by combining RAFT chain-growth polymerization and RAFT interchange.