Search Results (10)

In integrated circuit (IC) design, floorplanning is an important stage in obtaining the floorplan of the circuit to be designed. Floorplanning determines the performance, size, yield, and reliability of very large-scale integration circuit (VLSI) ICs. The results obtained in this step are necessary for the subsequent continuous processes of chip design. From a computational perspective, VLSI floorplanning is an NP-hard problem, making it difficult to be efficiently solved by classical optimization techniques. In this paper, we propose a deep reinforcement learning floorplanning algorithm based on sequence pairs (SP) to address the placement problem. Reinforcement learning utilizes an agent to explore the search space in sequence pairs to find the optimal solution. Experimental results on the international standard test circuit benchmarks, MCNC and GSRC, demonstrate that the proposed deep reinforcement learning floorplanning algorithm based on sequence pairs can produce a superior solution. Full article

An environmentally friendly Pickering stabilizer was developed by upcycling sugarcane bagasse (SCB) into a cellulose nanocrystal (CNC), which was subjected to surface modification by using quaternary ammonium compound to enhance its amphiphilic characteristics. The changes in microstructural properties of modified cellulose nanocrystal (m-CNC), such as surface functional group, thermal stability, surface morphology, elemental composition, and particle size distribution were investigated. Results indicated the success of quaternary ammonium compound grafting with the presence of a trimethyl-alkyl chain on the cellulose structure, while the m-CNC preserves the needle-like nanoparticles in length of ~534 nm and width of ~20 nm. The colloidal profile of m-CNC-stabilized oil–water emulsion gels with different concentrations of m-CNC (1–5 wt%), and oil:water (O:W) ratios (3:7, 5:5, 7:3) were examined. The emulsion gel stability study indicated that the optimal concentration of m-CNC (3 wt%) was able to stabilize all the emulsion gels at different O:W ratios with an emulsion index of >80% for 3 months. It is the minimum concentration of m-CNC to form a robust colloidal network around the small oil droplets, leading to the formation of stable emulsion gels. The emulsion gel with O:W ratio (3:7) with 3 wt% of m-CNC rendered the best m-CNC–oil-droplets dispersion. The m-CNC effectively retained the size of oil droplets (<10 μm for 3 months storage) against coalescence and creaming by creating a steric barrier between the two immiscible phases. Furthermore, the emulsion gel exhibited the highest viscosity and storage modulus which was able to prevent creaming or sedimentation of the emulsion gels. Full article

In this study, Fe-doped graphitic carbon nitride (Fe-MCNC) with varying Fe contents was synthesized via a supramolecular approach, followed by thermal exfoliation, and was then used for accelerated photocatalytic hydrogen evolution and nitrogen fixation. Various techniques were used to study the physicochemical properties of the MCN (g-C₃N₄ from melamine) and Fe-MCNC (MCN for g-C₃N₄ and C for cyanuric acid) catalysts. The field emission scanning electron microscopy (FE-SEM) images clearly demonstrate that the morphology of Fe-MCNC changes from planar sheets to porous, partially twisted (partially developed nanotube and nanorod) nanostructures. The elemental mapping study confirms the uniform distribution of Fe on the MCNC surface. The X-ray photoelectron spectroscopy (XPS) and UV-visible diffuse reflectance spectroscopy (UV-DRS) results suggest that the Fe species might exist in the Fe³⁺ state and form Fe-N bonds with N atoms, thereby extending the visible light absorption areas and decreasing the band gap of MCN. Furthermore, doping with precise amounts of Fe might induce exfoliation and increase the specific surface area, but excessive Fe could destroy the MCN structure. The optimized Fe-MCNC nanostructure had a specific surface area of 23.6 m² g⁻¹, which was 8.1 times greater than that of MCN (2.89 m² g⁻¹). To study its photocatalytic properties, the nanostructure was tested for photocatalytic hydrogen evolution and nitrogen fixation; 2Fe-MCNC shows the highest photocatalytic activity, which is approximately 13.3 times and 2.4 times better, respectively, than MCN-1H. Due to its high efficiency and stability, the Fe-MCNC nanostructure is a promising and ideal photocatalyst for a wide range of applications. Full article

Diclofenac sodium (DS), one of the most used non-steroidal anti-inflammatory drugs worldwide, is often detected in wastewater and natural water. This drug is ecotoxic, even at low concentrations. Therefore, it is essential to fabricate low-cost adsorbents that can easily and effectively remove DS from contaminated water bodies. In this study, a polyethyleneimine (PEI)-modified magnetic cellulose nanocrystal (MCNC) was prepared with a silane coupling agent as a bridge. TEM, FTIR, XRD, and VSM were used to demonstrate the successful preparation of MCNC-PEI. This composite adsorbent exhibited efficient DS removal. Furthermore, the adsorption performance of MCNC-PEI on DS was optimal under mildly acidic conditions (pH = 4.5). Adsorption kinetics showed that the adsorption process involves mainly electrostatic interactions. Moreover, the maximum adsorption capacity reached 299.93 mg/g at 25 °C, and the adsorption capacity only decreased by 9.9% after being reused five times. Considering its low cost, low toxicity, and high DS removal capacity, MCNC-PEI could be a promising adsorbent for treating DS-contaminated water. Full article

The nitration of chitin monomer in a mixture of nitric acid and acetic anhydride was conducted and a highly nitrated (3R,4R,6R)-3-acetamido-6-((nitrooxy)methyl)tetrahydro-2H-pyran-2,4,5-triyl trinitrate (1) was obtained. Its structure was fully characterized using infrared spectroscopy, NMR spectroscopy, elemental analysis, and X-ray diffraction. Compound 1 possesses good density (ρ: 1.721 g·cm⁻³) and has comparable detonation performance (V_d: 7717 m·s⁻¹; P: 25.6 GPa) to that of nitrocellulose (NC: V_d: 7456 m·s⁻¹; P: 23 GPa; I_sp = 239 s) and microcrystalline nitrocellulose (MCNC; V_d: 7683 m·s⁻¹; P: 25 GPa; I_sp = 250 s). However, Compound 1 has much lower impact sensitivity (IS: 15 J) than the regular nitrocellulose (NC; IS: 3.2 J) and MCNC (IS: 2.8 J). Compound 1 was calculated to exhibit a good specific impulse (I_sp: 240 s), which is comparable with NC (I_sp: 239 s) and MCNC (I_sp: 250 s). By replacing the nitrocellulose with Compound 1 in typical propellants JA2, M30, and M9, the specific impulse was improved by up to 4 s. These promising properties indicate that Compound 1 has a significant potential as an energetic component in solid propellants. Full article

Different modification process routes are used to improve the modified cellulose nanocrystalline (MCNC) with higher fatty acid by esterification reaction and graft polymerization to obtain certain hydrophobic properties. Two preparation methods, product structure and surface activity, are compared and explored. Experimental results show that the modified product is still at the nanometer level and basically retains the crystal structure of the raw cellulose nanocrystalline (CNC). The energy consumption of the two preparation methods is low; however, the esterification method with co-reactant requires short reaction time, and the degree of substitution of the product is high. The modified product prepared by grafting polymerization method has a high HLB value and amphiphilicity, which can effectively reduce the surface tension of water. Therefore, it can be used as a green and environmentally friendly surface-active substance. Full article

The presence of toxic metals in surface and natural waters, even at trace levels, poses a great danger to humans and the ecosystem. Although the combination of adsorption and coagulation techniques has the potential to eradicate this problem, the use of inappropriate media remains a major drawback. This study reports on the application of NaNO₂/NaHCO₃ modified sawdust-based cellulose nanocrystals (MCNC) as both coagulant and adsorbent for the removal of Cu, Fe and Pb from aqueous solution. The surface modified coagulants, prepared by electrostatic interactions, were characterized using Fourier transform infrared, X-ray diffraction (XRD), and scanning electron microscopy/energy-dispersive spectrometry (SEM/EDS). The amount of coagulated/adsorbed trace metals was then analysed using inductively coupled plasma atomic emission spectroscopy (ICP-AES). SEM analysis revealed the patchy and distributed floccules on Fe-flocs, which was an indication of multiple mechanisms responsible for Fe removal onto MCNC. A shift in the peak position attributed to C₂H₁₉₂N₆₄O₁₆ from 2θ = 30 to 24.5° occurred in the XRD pattern of both Pb- and Cu-flocs. Different process variables, including initial metal ions concentration (10–200 mg/L), solution pH (2–10), and temperature (25–45 °C) were studied in order to investigate how they affect the reaction process. Both Cu and Pb adsorption followed the Langmuir isotherm with a maximum adsorption capacity of 111.1 and 2.82 mg/g, respectively, whereas the adsorption of Fe was suggestive of a multilayer adsorption process; however, Fe Langmuir maximum adsorption capacity was found to be 81.96 mg/g. The sequence of trace metals removal followed the order: Cu > Fe > Pb. The utilization of this product in different water matrices is an effective way to establish their robustness. Full article

In this paper, we address an NPN Boolean matching algorithm. The proposed structural difference signature (SDS) of a Boolean function significantly reduces the search space in the Boolean matching process. The paper analyses the size of the search space from three perspectives: the total number of possible transformations, the number of candidate transformations and the number of decompositions. We test the search space and run time on a large number of randomly generated circuits and Microelectronics Center of North Carolina (MCNC) benchmark circuits with 7–22 inputs. The experimental results show that the search space of Boolean matching is greatly reduced and the matching speed is obviously accelerated. Full article

Recently, concurrent error detection enabled through invariant relationships between different wires in a circuit has been proposed. Because there are many such implications in a circuit, selection strategies have been developed to select the most valuable implications for inclusion in the checker hardware such that a sufficiently high probability of error detection ( $P_{detection}$ ) is achieved. These algorithms, however, due to their heuristic nature cannot guarantee a lossless $P_{detection}$ . In this paper, we develop a new input-aware implication selection algorithm with the help of ATPG which minimizes loss on $P_{detection}$ . In our algorithm, the detectability of errors for each candidate implication is carefully evaluated using error prone vectors. The evaluation results are then utilized to select the most efficient candidates for achieving optimal $P_{detection}$ . The experimental results on 15 representative combinatorial benchmark circuits from the MCNC benchmarks suite show that the implications selected from our algorithm achieve better $P_{detection}$ in comparison to the state of the art. The proposed method also offers better performance, up to 41.10%, in terms of the proposed impact-level metric, which is the ratio of achieved $P_{detection}$ to the implication count. Full article

This paper describes the stochastic-based Spin-Programmable Gate Array (SPGA), an innovative architecture attempting to exploit the stochastic switching behavior newly found in emerging spintronic devices for reconfigurable computing. While many recently studies have investigated using Spin Transfer Torque Memory (STTM) devices to replace configuration memory in field programmable gate arrays (FPGAs), our study, for the first time, attempts to use the quantum-induced stochastic property exhibited by spintronic devices directly for reconfiguration and logic computation. Specifically, the SPGA was designed from scratch for high performance, routability, and ease-of-use. It supports variable-granularity multiple-input-multiple-output (MIMO) logic blocks and variable-length bypassing interconnects with a symmetrical structure. Due to its unconventional architectural features, the SPGA requires several major modifications to be made in the standard VPR placement/routing CAD flow, which include a new technology mapping algorithm based on computing (k, l)-cut, a new placement algorithm, and a modified delay-based routing procedure.Previous studies have shown that, simply replacing reconfiguration memory bits with spintronic devices, the conventional 2D island-style FPGA architecture can achieve approximately 5 times area savings, 2 times speedup and 1.6 times power savings. Our mixed-mode simulation results have shown that, with FPGA architecture innovations, on average, a SPGA can further achieve more than 10 times improvement in logic density, about 5 times improvement in average net delay, and about 5 times improvement in the critical-path delay for the largest 12 MCNC benchmark circuits over an island-style baseline FPGA with spintronic configuration bits. Full article