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Abstract Au nanoclusters often demonstrate useful optical properties such as visible/near‐infrared photoluminescence, in addition to remarkable thermodynamic stability owing to their superatomic behavior. The smallest of the 8e⁻superatomic Au nanoclusters, Au₁₁, has limited applications due to its lack of luminescence and relatively low stability. In this work, we investigate the introduction of a single Pt dopant to the center of a halide‐ and triphenylphosphine‐ligated Au₁₁nanocluster, affording a cluster with a proposed molecular formula PtAu₁₀(PPh₃)₇Br₃. Electrochemical and spectroscopic analysis reveal an expansion of the HOMO–LUMO gap due to the Pt dopant, as well as relatively strong near‐infrared (NIR) photoluminescence which is atypical for an M₁₁cluster (λ_max= 700 nm, Φ = 1.88 %). The Pt dopant additionally boosted photostability; more than tenfold. Lastly, we demonstrate the application of the PtAu₁₀cluster's NIR photoluminescence in the detection of the nitroaromatic compound 2,4‐dinitrotoluene, with a limit‐of‐detection of 9.52 μM (1.74 ppm). The notable ability of a single central Pt dopant to unlock photoluminescence in a non‐luminescent nanocluster highlights the advantages of heterometal doping in the tuning of both the optical and thermodynamic properties of Au nanoclusters. 

Sponge-grade Archaeocyatha were early Cambrian biomineralizing metazoans that constructed reefs globally. Despite decades of research, many facets of archaeocyath palaeobiology remain unclear, making it difficult to reconstruct the palaeoecology of Cambrian reef ecosystems. Of specific interest is how these organisms fed; previous experimental studies have suggested that archaeocyaths functioned as passive suspension feeders relying on ambient currents to transport nutrient-rich water into their central cavities. Here, we test this hypothesis using computational fluid dynamics (CFD) simulations of digital models of select archaeocyath species. Our results demonstrate that, given a range of plausible current velocities, there was very little fluid circulation through the skeleton, suggesting obligate passive suspension feeding was unlikely. Comparing our simulation data with exhalent velocities collected from extant sponges, we infer an active suspension feeding lifestyle for archaeocyaths. The combination of active suspension feeding and biomineralization in Archaeocyatha may have facilitated the creation of modern metazoan reef ecosystems. 

Using flash-based solid state drives (SSDs) as main memory has been proposed as a practical solution towards scaling memory capacity for data-intensive applications. However, almost all existing approaches rely on the paging mechanism to move data between SSDs and host DRAM. This inevitably incurs significant performance overhead and extra I/O traffic. Thanks to the byte-addressability supported by the PCIe interconnect and the internal memory in SSD controllers, it is feasible to access SSDs in both byte and block granularity today. Exploiting the benefits of SSD's byte-accessibility in today's memory-storage hierarchy is, however, challenging as it lacks systems support and abstractions for programs. In this paper, we present FlatFlash, an optimized unified memory-storage hierarchy, to efficiently use byte-addressable SSD as part of the main memory. We extend the virtual memory management to provide a unified memory interface so that programs can access data across SSD and DRAM in byte granularity seamlessly. We propose a lightweight, adaptive page promotion mechanism between SSD and DRAM to gain benefits from both the byte-addressable large SSD and fast DRAM concurrently and transparently, while avoiding unnecessary page movements. Furthermore, we propose an abstraction of byte-granular data persistence to exploit the persistence nature of SSDs, upon which we rethink the design primitives of crash consistency of several representative software systems that require data persistence, such as file systems and databases. Our evaluation with a variety of applications demonstrates that, compared to the current unified memory-storage systems, FlatFlash improves the performance for memory-intensive applications by up to 2.3x, reduces the tail latency for latency-critical applications by up to 2.8x, scales the throughput for transactional database by up to 3.0x, and decreases the meta-data persistence overhead for file systems by up to 18.9x. FlatFlash also improves the cost-effectiveness by up to 3.8x compared to DRAM-only systems, while enhancing the SSD lifetime significantly. 

Recent advancements in deep learning techniques facilitate intelligent-query support in diverse applications, such as content-based image retrieval and audio texturing. Unlike conventional key-based queries, these intelligent queries lack efficient indexing and require complex compute operations for feature matching. To achieve high-performance intelligent querying against massive datasets, modern computing systems employ GPUs in-conjunction with solid-state drives (SSDs) for fast data access and parallel data processing. However, our characterization with various intelligent-query workloads developed with deep neural networks (DNNs), shows that the storage I/O bandwidth is still the major bottleneck that contributes 56%--90% of the query execution time. To this end, we present DeepStore, an in-storage accelerator architecture for intelligent queries. It consists of (1) energy-efficient in-storage accelerators designed specifically for supporting DNN-based intelligent queries, under the resource constraints in modern SSD controllers; (2) a similarity-based in-storage query cache to exploit the temporal locality of user queries for further performance improvement; and (3) a lightweight in-storage runtime system working as the query engine, which provides a simple software abstraction to support different types of intelligent queries. DeepStore exploits SSD parallelisms with design space exploration for achieving the maximal energy efficiency for in-storage accelerators. We validate DeepStore design with an SSD simulator, and evaluate it with a variety of vision, text, and audio based intelligent queries. Compared with the state-of-the-art GPU+SSD approach, DeepStore improves the query performance by up to 17.7×, and energy-efficiency by up to 78.6×.