Irreversible coherent matching bonding of van der Waals heterostructure lattice by pressure.

The key of heterostructure is the combinations created by stacking various vdW materials, which can modify interlayer coupling and electronic properties, providing exciting opportunities for designer devices. However, this simple stacking does not create chemical bonds, making it difficult to fundamentally alter the electronic structure. Here, we demonstrate that interlayer interactions in heterostructures can be fundamentally controlled using hydrostatic pressure, providing a bonding method to modify electronic structures. By covering graphene with boron nitride and inducing an irreversible phase transition, the conditions for graphene lattice-matching bonding (IMB) were created. We demonstrate that the increased bandgap of graphene under pressure is well maintained in ambient due to the IMB in the interface. Comparison to theoretical modeling emphasizes the process of pressure-induced interfacial bonding, systematically generalizes, and predicts this model. Our results demonstrate that pressure can irreversibly control interlayer bonding, providing opportunities for high-pressure technology in ambient applications and IMB engineering in heterostructures.

Strain-induced electronic structures and band-gap of few-layer AgInP2S6.

The band gap and mechanical control ability of two-dimensional materials largely determine the application value of two-dimensional devices in optical and electronic properties, so the bandgap controllability of two-dimensional materials broadens the application range of multi-functional devices. In the layered van der Waals (vdW) material AgInP2S6, the band gap can be adjusted by the number of layers and flexible strain, and the few layers AgInP2S6have discrete band gap values, which are also relevant for optoelectronic applications. In the strain range of up to 2.7% applied, the band gap can be adjusted, and the film is relatively stable under strain. We further analyzed the physical mechanism of flexible strain band gap regulation and found that strain-regulation reduced the band gap and increased the chemical bond length. These studies open up new opportunities for the future development of vdW material photoelectric resonators represented by AgInP2S6, and have important reference value.

Biological thermometer based on the temperature sensitivity of magnetic nanoparticle paraSHIFT.

In the paper, the temperature dependence of magnetic nanoparticle (MNP) paramagnetic chemical shift (paraSHIFT) was studied by magnetic resonance (MR) spectroscopy. Based on it, iron oxide MNPs are considered as MR shifting probes for determining the temperature in liquids. With the increase in measurement temperature of the MNP reagent with MNPs, the decrease of MNP magnetization would make the peak of spectroscopy shift to the higher chemical shift area. The peak shift is related to the magnetic susceptibility of MNPs, which can be determined by MR frequency as a function of temperature and particle size. Experiments on temperature-dependent chemical shifts are performed for MNP samples with different core sizes and the estimated temperature accuracy can achieve 0.1 K. Combined with the contrast effect of magnetic nanoparticles in magnetic resonance imaging at 3 T, this technology can realize temperature imaging.

Iron oxide magnetic nanoparticles based low-field MR thermometry.

This paper reports on a highly accurate approach of magnetic resonance (MR) thermometry using iron oxide magnetic nanoparticles (MNPs) as temperature sensors. An empirical model for the description of the temperature dependent R 2 relaxation rate is proposed by taking into account the temperature sensitivity of the MNP magnetization. The temperature sensitivity of the MNP magnetization (η) and the temperature sensitivity of the R 2 relaxation rate (κ) are simulated with the proposed empirical models to investigate their dependence on the magnetic field and the particle size. Simulation results show the existence of optimal magnetic fields Hoη and Hoκ that maximize the temperature sensitivities η and κ. Furthermore, simulations and experiments demonstrate that the optimal magnetic field Hoη (Hoκ ) decreases with increasing the particle size. Experiments on temperature dependent R 2 relaxation rate are performed at different magnetic fields for MNP samples with different iron concentrations. Experimental results show that the proposed MR thermometry using MNPs as temperature sensors allows a temperature estimation accuracy of about 0.05 °C. We believe that the achieved approach of highly accurate MR thermometry is of great interest and significance to biomedicine and biology.

A High-Energy, Wide-Spectrum, Spatiotemporal Mode-Locked Fiber Laser.

In this article, we demonstrate a high-energy, wide-spectrum, spatiotemporal mode-locked (STML) fiber laser. Unlike traditional single-mode fiber lasers, STML fiber lasers theoretically enable mode-locking with various combinations of transverse modes. The laser can deliver two different STML pulse sequences with different pulse widths, spectra and beam profiles, due to the different compositions of transverse modes in the output pulses. Moreover, we achieve a wide-spectrum pulsed output with a single-pulse energy of up to 116 nJ, by weakening the spectral filtering and utilizing self-cleaning. Strong spatial and spectral filtering are usually thought to be necessary for achieving STML. Our experiment verifies the necessity of spatial filtering for achieving STML, and we show that weakening unnecessary spectral filtering provides an effective way to increase the pulse energy and spectrum width of mode-locked fiber lasers.

Enhancing biomass conversion to bioenergy with machine learning: Gains and problems.

The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.

Improving magnetic nanothermometry accuracy through mixing-frequency excitation.

This study proposes a temperature model for the relaxation of magnetic nanoparticles and a phase measurement method under a mixing-frequency excitation field, which can improve the accuracy of temperature measurements in magnetic nanothermometry. According to the Debye-based magnetization model for magnetic nanoparticles, phases at mixing frequencies are used to solve the problem of a delay in the relaxation phase of the magnetic field at a high frequency. This method can improve the signal-to-noise ratio of the response of the magnetic nanoparticles and weaken the phase shift of the detection coils caused by the changes in temperature. The results of experiments show that the proposed method can achieve static temperature measurement error less than 0.1 K and dynamic temperature measurement error less than 0.2 K.