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In this work, we compared six emerging mobile laser scanning (MLS) technologies for field reference data collection at the individual tree level in boreal forest conditions. The systems under study were an in-house developed AKHKA-R3 backpack laser scanner, a handheld Zeb-Horizon laser scanner, an under-canopy UAV (Unmanned Aircraft Vehicle) laser scanning system, and three above-canopy UAV laser scanning systems providing point clouds with varying point densities. To assess the performance of the methods for automated measurements of diameter at breast height (DBH), stem curve, tree height and stem volume, we utilized all of the six systems to collect point cloud data on two 32 m-by-32 m test sites classified as sparse (n = 42 trees) and obstructed (n = 43 trees). To analyze the data collected with the two ground-based MLS systems and the under-canopy UAV system, we used a workflow based on our recent work featuring simultaneous localization and mapping (SLAM) technology, a stem arc detection algorithm, and an iterative arc matching algorithm. This workflow enabled us to obtain accurate stem diameter estimates from the point cloud data despite a small but relevant time-dependent drift in the SLAM-corrected trajectory of the scanner. We found out that the ground-based MLS systems and the under-canopy UAV system could be used to measure the stem diameter (DBH) with a root mean square error (RMSE) of 2–8%, whereas the stem curve measurements had an RMSE of 2–15% that depended on the system and the measurement height. Furthermore, the backpack and handheld scanners could be employed for sufficiently accurate tree height measurements (RMSE = 2–10%) in order to estimate the stem volumes of individual trees with an RMSE of approximately 10%. A similar accuracy was obtained when combining stem curves estimated with the under-canopy UAV system and tree heights extracted with an above-canopy flying laser scanning unit. Importantly, the volume estimation error of these three MLS systems was found to be of the same level as the error corresponding to manual field measurements on the two test sites. To analyze point cloud data collected with the three above-canopy flying UAV systems, we used a random forest model trained on field reference data collected from nearby plots. Using the random forest model, we were able to estimate the DBH of individual trees with an RMSE of 10–20%, the tree height with an RMSE of 2–8%, and the stem volume with an RMSE of 20–50%. Our results indicate that ground-based and under-canopy MLS systems provide a promising approach for field reference data collection at the individual tree level, whereas the accuracy of above-canopy UAV laser scanning systems is not yet sufficient for predicting stem attributes of individual trees for field reference data with a high accuracy.

Scanning transmission electron microscopy (STEM) is widely used for imaging, diffraction, and spectroscopy of materials down to atomic resolution. Recent advances in detector technology and computational methods have enabled many experiments that record a full image of the STEM probe for many probe positions, either in diffraction space or real space. In this paper, we review the use of these four-dimensional STEM experiments for virtual diffraction imaging, phase, orientation and strain mapping, measurements of medium-range order, thickness and tilt of samples, and phase contrast imaging methods, including differential phase contrast, ptychography, and others.

The bacterium Sporosarcina pasteurii (SP) is known for its ability to cause the phenomenon of microbially induced calcium carbonate precipitation (MICP). We explored bacterial participation in the initial stages of the MICP process at the cellular length scale under two different growth environments (a) liquid culture (b) MICP in a soft agar (0.5%) column. In the liquid culture, ex-situ imaging of the cellular environment indicated that S. pasteurii was facilitating nucleation of nanoscale crystals of calcium carbonate on bacterial cell surface and its growth via ureolysis. During the same period, the meso-scale environment (bulk medium) was found to have overgrown calcium carbonate crystals. The effect of media components (urea, CaCl2), presence of live and dead in the growth medium were explored. The agar column method allows for in-situ visualization of the phenomena, and using this platform, we found conclusive evidence of the bacterial cell surface facilitating formation of nanoscale crystals in the microenvironment. Here also the bulk environment or the meso-scale environment was found to possess overgrown calcium carbonate crystals. Extensive elemental analysis using Energy dispersive X-ray spectroscopy (EDS) and X-ray powder diffraction (XRD), confirmed that the crystals to be calcium carbonate, and two different polymorphs (calcite and vaterite) were identified. Active participation of S. pasteurii cell surface as the site of calcium carbonate precipitation has been shown using EDS elemental mapping with Scanning transmission electron microscopy (STEM) and scanning electron microscopy (SEM).

Atomically thin van der Waals materials stacked with an interlayer twist have proven to be an excellent platform toward achieving gate-tunable correlated phenomena linked to the formation of flat electronic bands. In this work we demonstrate the formation of emergent correlated phases in multilayer rhombohedral graphene—a simple material that also exhibits a flat electronic band edge but without the need of having a moiré superlattice induced by twisted van der Waals layers. We show that two layers of bilayer graphene that are twisted by an arbitrary tiny angle host large (micrometer-scale) regions of uniform rhombohedral four-layer (ABCA) graphene that can be independently studied. Scanning tunneling spectroscopy reveals that ABCA graphene hosts an unprecedentedly sharp van Hove singularity of 3–5-meV half-width. We demonstrate that when this van Hove singularity straddles the Fermi level, a correlated many-body gap emerges with peak-to-peak value of 9.5 meV at charge neutrality. Mean-field theoretical calculations for model with short-ranged interactions indicate that two primary candidates for the appearance of this broken symmetry state are a charge-transfer excitonic insulator and a ferrimagnet. Finally, we show that ABCA graphene hosts surface topological helical edge states at natural interfaces with ABAB graphene which can be turned on and off with gate voltage, implying that small-angle twisted double-bilayer graphene is an ideal programmable topological quantum material.

Four-wheeled test drive Any future artificial transporters and robots operating at the nanoscale are likely to require molecules capable of directional translational movement over a surface. Even the design of such molecules is a daunting task, however, as they need to be able to use light, chemical or electrical energy to modulate their interaction with the surface in a way that generates directional motion. Kudernac et al . now unveil just such a molecule, made by attaching four rotary motor units to a central axis. Inelastic electron tunnelling induces conformational changes in the rotors and propels the molecule across a copper surface. By changing the direction of the rotary motion of individual motor units, the self-propelling molecular 'four-wheeler' structure can follow random or preferentially linear trajectories. This design provides a starting point for the exploration of more sophisticated molecular mechanical systems, perhaps with complete control over their direction of motion. Propelling single molecules in a controlled manner along an unmodified surface remains extremely challenging because it requires molecules that can use light, chemical or electrical energy to modulate their interaction with the surface in a way that generates motion. Nature’s motor proteins 1 , 2 have mastered the art of converting conformational changes into directed motion, and have inspired the design of artificial systems 3 such as DNA walkers 4 , 5 and light- and redox-driven molecular motors 6 , 7 , 8 , 9 , 10 , 11 . But although controlled movement of single molecules along a surface has been reported 12 , 13 , 14 , 15 , 16 , the molecules in these examples act as passive elements that either diffuse along a preferential direction with equal probability for forward and backward movement or are dragged by an STM tip. Here we present a molecule with four functional units—our previously reported rotary motors 6 , 8 , 17 —that undergo continuous and defined conformational changes upon sequential electronic and vibrational excitation. Scanning tunnelling microscopy confirms that activation of the conformational changes of the rotors through inelastic electron tunnelling propels the molecule unidirectionally across a Cu(111) surface. The system can be adapted to follow either linear or random surface trajectories or to remain stationary, by tuning the chirality of the individual motor units. Our design provides a starting point for the exploration of more sophisticated molecular mechanical systems with directionally controlled motion.

In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

Leaf area index (LAI) is an important vegetation parameter. Active light detection and ranging (LiDAR) technology has been widely used to estimate vegetation LAI. In this study, LiDAR technology, LAI retrieval and validation methods, and impact factors are reviewed. First, the paper introduces types of LiDAR systems and LiDAR data preprocessing methods. After introducing the application of different LiDAR systems, LAI retrieval methods are described. Subsequently, the review discusses various LiDAR LAI validation schemes and limitations in LiDAR LAI validation. Finally, factors affecting LAI estimation are analyzed. The review presents that LAI is mainly estimated from LiDAR data by means of the correlation with the gap fraction and contact frequency, and also from the regression of forest biophysical parameters derived from LiDAR. Terrestrial laser scanning (TLS) can be used to effectively estimate the LAI and vertical foliage profile (VFP) within plots, but this method is affected by clumping, occlusion, voxel size, and woody material. Airborne laser scanning (ALS) covers relatively large areas in a spatially contiguous manner. However, the capability of describing the within-canopy structure is limited, and the accuracy of LAI estimation with ALS is affected by the height threshold and sampling size, and types of return. Spaceborne laser scanning (SLS) provides the global LAI and VFP, and the accuracy of estimation is affected by the footprint size and topography. The use of LiDAR instruments for the retrieval of the LAI and VFP has increased; however, current LiDAR LAI validation studies are mostly performed at local scales. Future research should explore new methods to invert LAI and VFP from LiDAR and enhance the quantitative analysis and large-scale validation of the parameters.

Background/Objectives: Intraoral scanning, a fast-evolving technology, is increasingly integrated into actual dental workflows due to its numerous advantages. Despite its growing adoption, challenges related to the accuracy of digital impressions remain. The existing literature identifies most of the factors influencing intraoral scanning accuracy (defined by precision and trueness), but it is fragmented and lacks a unified synthesis. In response to this gap, the present study aims to consolidate and structure the current evidence on the determinant factors and, based on these findings, to develop a clinically applicable procedural guideline for dental practitioners. Methods: A comprehensive literature review identified 43 distinct factors influencing intraoral scanning. Results: These factors encompass variables such as software versions and updates, implant characteristics (e.g., position, angulation, scan body design), materials, environmental conditions (e.g., lighting), and procedural elements including scanning strategy, pattern, aids, and operator experience. Subsequently, these identified factors were systematically classified into five distinct groups based on inherent similarities and relevance within the scanning workflow: IOS—characteristics and maintenance, intraoral morphology, materials, ambient conditions, and scanning strategy. To translate these findings into a practical framework, a four-step protocol was developed, designed for straightforward application by researchers and clinicians. Conclusions: This protocol—comprising: (1) Maintenance, (2) Evaluation, (3) Establishment and Execution of Scanning Strategy, and (4) Verification—aims to guide users effectively through the intraoral scanning process, mitigate common clinical challenges, and ensure broad applicability across diverse scanner systems, irrespective of the manufacturer or model.

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes—ranging from endoplasmic reticulum to microtubules to ribosomes—in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM) 1 . We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, ‘OpenOrganelle’, to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets. Focused ion beam scanning electron microscopy (FIB-SEM) combined with deep-learning-based segmentation is used to produce three-dimensional reconstructions of complete cells and tissues, in which up to 35 different organelle classes are annotated.