We present a new iteration of smart active matter modules capable of unprecedented 3D entanglement, designed specifically for fabrication and operation at large scales by a range of scientific users. We discuss the benefits of entanglement compared to traditional rigid, lattice formations in active matter and modular robots, and the design which supports low cost, a small and appropriate form factor, low weight, low barrier-of-entry, and ease of operation. We characterize the platform in terms of actuation repeatability and longevity, lifting and holding strength, a number of sensing modalities, and battery life. We demonstrate short and (relatively) long range communication using tactile and acoustic transceivers. We further show exploratory collective behaviors with up to 10 modules, including static entanglement and self disassembly. We hope that this open-source ‘robo-physical’ platform can pave the way for new innovations across the fields of modular robots and active and soft matter.

In this study, we present a digital microfluidic (DMF) system that connects analyte droplets with planar microcoils in a high-field nuclear magnetic resonance (NMR) spectrometer for in situ photoreaction analysis. Transparent indium tin oxide glass was employed in fabricating the DMF electrode chips. NMR spectroscopy was utilized for its superior sensitivity and ability to detect small changes in molecular structure, making it highly advantageous for chemical analysis. Utilizing Computer Simulation Technology Studio Suite, we designed a plane annular microstrip coil that accurately matches droplet shape to optimize detection sensitivity and increase filling factor. An advanced DMF control circuit remotely manages droplet samples and facilitates precise mixing for in situ reaction analysis. Our results demonstrate improved sensitivity in the in situ experimental analysis, such as the aldehyde amine reaction (sodium benzaldehyde-2-sulfonate solution and aniline solution) and photopolymerization reaction (polyethylene glycol ester). The integration of DMF and NMR technologies provides a promising approach for increasing efficiency and accuracy in chemical analysis.

The authors regret that some incorrect images were and 0.01 mM) during the assembling of Figure 3b. In addition, the unit for the vertical axis in Figure 3a should read ???s???1??? rather than ???mM???1 s???1???. The corrected Figure 3 is shown in the following. These corrections do not affect the interpretation of the data or change the conclusions of the article. The authors apologize for any inconvenience caused by these

Multi-robot systems have been shown to build large-scale, user-specified structures using distributed, environmentally-mediated coordination in simulation. Little attention, however, has been devoted to error propagation and mitigation. In this paper, we introduce a detailed simulation of TERMES, a prototypical construction system, in which robots have realistic error profiles. We use this simulator and 32 randomly generated 250-brick blueprints to show that action errors can have significant long-term effects. We study the spatio-temporal error distribution and introduce and characterize the efficacy of a simple decay-based error correction mechanism. Although inefficient, this type of error correction is promising because it can be performed by robots with the same limited sensory capabilities as those who place bricks. To limit the impact on the construction rate, we also examine decay mechanisms informed by spatial and temporal error distributions. The incorporation of decay in our building process increases the probability of successful completion by ~ 4, at the expense of ~1/4 decrease in construction rate.

We investigate the effect of errors in collective robotic construction (CRC) on both construction time and the probability of correctly completing a specified structure. We ground our investigation in the TERMES distributed construction system, which uses local sensing and stigmergic rules that enable robots to navigate and build 3D structures. We perform an in depth analysis and categorization of action failures in CRC systems. We present an approach to mitigating action failures and preventing errors that prohibit completion of a structure by adding predictive local checks. We show that the predictive local checks can increase the probability of success by orders of magnitude in large structures. This work demonstrates the need to consider both construction time and the effect of errors in collective robotic construction.

Implantable brain machine interfaces for treatment of neurological disorders require on-chip, real-time signal processing of action potentials (spikes). In this work, we present the first spike sorting SoC with integrated neural recording front-end and analog unsupervised classifier. The event-driven, low power spike sorter features a novel hardware-optimized, K-means based algorithm that effectively eliminates duplicate clusters and is implemented using a novel clockless and ADC-less analog architecture. The 1.4 mm2 chip is fabricated in a 180-nm CMOS SOI process. The analog front-end achieves a 3.3 μVrms noise floor over the spike bandwidth (400 - 5000 Hz) and consumes 6.42 μW from a 1.5 V supply. The analog spike sorter consumes 4.35 μW and achieves 93.2% classification accuracy on a widely used synthetic test dataset. In addition, higher than 93% agreement between the chip classification result and that of a standard spike sorting software is observed using pre-recorded real neural signals. Simulations of the implemented spike sorter show robust performance under process-voltage-temperature variations.

We present the design, fabrication, characterization, and optimization of a TPM (twin parallel microstrip)-based nuclear magnetic resonance (NMR) probe, produced by using a low-loss Teflon PTFE F4B high frequency circuit board. We use finite element analysis to optimize the radio frequency (RF) homogeneity and sensitivity of the TPM probe jointly for various sample volumes. The RF homogeneity of this TPM planar probe is superior to that of only a single microstrip probe. The optimized TPM probe properties such as RF homogeneity and field strength are characterized experimentally and discussed in detail. By combining this TPM based NMR probe with microfluidic technology, the sample amount required for kinetic study using NMR spectroscopy was minimized. This is important for studying costly samples. The TPM NMR probes provide high sensitivity to analysis of 5 µl samples with 2 mM concentrations within 10 min. The miniaturized microfluidic NMR probe plays an important role in realizing down to seconds timescale for kinetic monitoring.

Multifunctional nanostructures with both diagnostic and therapeutic capabilities have attracted considerable attention in biomedical research because they can offer great advantages in disease management and prognosis. In this work, a facile way to transfer the hydrophobic iron oxide (IO) nanoparticles into aqueous media by employing carboxylic graphene oxide (GO-COOH) as the transferring agent has been reported. In this one-step process, IO nanoparticles adhere to GO-COOH and form water-dispersible clusters via hydrophobic interactions between the hydrophobic ligands of IO nanoparticles and the basal plane of GO-COOH. The multiple IO nanoparticles on GO-COOH sheets (IO/GO-COOH) present a significant increase in T2 contrast enhancement. Moreover, the IO/GO-COOH nanoclusters also display a high photothermal conversion efficiency and can effectively inhibit tumor growth through the photothermal effects. It is envisioned that such IO/GO-COOH nanocomposites combining efficient MRI and photothermal therapy hold great promise in theranostic applications.

Attempts on developing the method for qualifying digital image correlation (DIC) method results, especial the displacement field, is presented in this paper. By taking a series images from center and margin of imaging field, DIC method is implemented to calculate displacement fields with respect to center of imaging field as origin of movement. Concurrently, residual displacements are obtained by subtracting mean value from the obtained displacement fields. The residual displacements are considered to be errors and mainly introduced by the imaging system of DIC. In this paper, modulation transfer function (MTF) is adopted to relate the residual displacement, DIC quality, with imaging system performance. By investing the MTFs cross-correlation values of all positions with respect to the reference image, a potential qualitative method for inspecting the DIC results based on sub-image MTF cross-correlation is proposed in this paper.

Solar energy is one of the most important alternative energy resources to the traditional fossil fuel energy. Since solar energy merely utilizes the solar radiation lights inducted from the sun, which are endless and available periodically to the earth planet, it is considered a renewable energy. It does not depend on the geographic location of the specific sources and is available everywhere in all countries in the planet. Since solar energy is completely natural, it is also considered a clean energy source. It does not disrupt the environment or create a threat to our eco-systems the way fossil fuel and some other energy sources might. It does not cause greenhouse gases, air or water pollution. With the development of solar technology in the past decades, solar energy becomes an economically affordable energy source and attracts more and more countries to include it in their national strategy for development.

The innovative applications of engineered nanoparticles (NPs) in medicine, such as diagnosis and therapy, have attracted considerable attention. It is highly important to predict the interactions between engineered NPs and the complex biological system as well as the impacts on the subsequent behaviors in living subjects. Herein, we report the use of T1 contrast-enhanced magnetic resonance imaging (MRI) to monitor the in vivo behaviors of NPs in a real-time manner. We chose ultrasmall Pd nanosheets (SPNSs) as the object of NPs because of their promise in theranostics and fitness for diverse surface chemistry. SPNSs were modified with different surface coating ligands (e.g., polyethylene glycol, zwitterionic ligands, polyethylenimine) and functionalized with Gd-chelates to render T1 contrast-enhanced capability. MRI real-time monitoring recorded the location and accumulation of SPNSs in small animals and revealed the prominent roles of surface coating ligands in pharmacokinetics. These results highlighted the significance of selecting proper surface coating for particular biomedical assignment. Moreover, we demonstrated a powerful and noninvasive means to predict and detect the behaviors of NPs in living subjects, which may be helpful for rational design and screening of engineered NPs in biomedical applications.

Three multinuclear gadolinium(iii) clusters were synthesized and characterized, and their potential as MRI contrast agents was evaluated at 7 T.

Precise nodal staging is particularly important to guide the treatments and determine the prognosis for cancer patients. However, it is still challenging to noninvasively and precisely detect in-depth tumor metastasis in lymph nodes (LNs) because of the small size and high potential of obtaining pseudopositive results. Herein, we report the rational design of a T-1-T-2 dual-modal MRI contrast agent for accurate imaging of tumor metastasis in LNs using gadolinium-embedded iron oxide nanoplates (GdIOP). The GdIOP were modulated with suitable size in vivo through surface functionalization by zwitterionic dopamine sulfonate (ZDS) molecules. The efficient uptake of GdIOP@ZDS nanoparticles through drainage effect because of the presence of large amount of macrophages and dendritic cells generates both T-1 and T-2 contrasts in LNs. In contrast, the low uptake of protein-corona-free GdIOP@ZDS nanoparticles by melanoma B16 tumor cells promises pseudocontrast imaging of potential tumor metastasis in LNs. The combination of T-1 and T-2 imaging modalities allows self-confirmed detection of a metastatic tumor with about 1.2 mm in the minimal dimension in LNs, which is close to the detection limit of submilimeter level of MRI scans. This study provides an efficient and noninvasive strategy to detect tumor metastasis in LNs with greatly enhanced diagnostic accuracy.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

In this paper, we demonstrate the tunable T-1 and T-2 contrast abilities of engineered iron oxide nanoparticles with high performance for liver contrast-enhanced magnetic resonance imaging (MRI) in mice. To enhance the diagnostic accuracy of MRI, large numbers of contrast agents with T-1 or T-2 contrast ability have been widely explored. The comprehensive investigation of high-performance MRI contrast agents with controllable T-1 and T-2 contrast abilities is of high importance in the field of molecular imaging. In this study, we synthesized uniform manganese-doped iron oxide (MnIO) nanoparticles with controllable size from 5 to 12 nm and comprehensively investigated their MRI contrast abilities. We revealed that the MRI contrast effects of MnIO nanoparticles are highly size-dependent. By controlling the size of MnIO nanoparticles, we can achieve T-1-dominated, T-2-dominated, and T-1-T-2 dual-mode MRI contrast agents with much higher contrast enhancement than the corresponding conventional iron oxide nanoparticles.

In this study, deformation of cylindrical shells under axial compressive load was studied and characterized by a noncontact detection technique, called digital image correlation (DIC). As opposed to commonly used strain gages for measuring structure strains at specific points, the DIC method can render not only 2D but also 3D full-field measurements for strain as well as structure deformation. The accuracy of strain measurement obtained using the DIC method was carefully validated by following ASTM standard E8 for strain measurement using strain gages in tensile tests. The DIC technique provided convenient measurements for characterizing the buckling behaviors of defective cylindrical shell samples. This study has engineering implications for providing 3D strain and deformation analyses to ensure structure reliability and safety.

Microelectronic devices with high integration level and functional complexity are always requiring larger diameter and more perfect Czochralski (CZ) silicon wafers. Therefore, the defects, playing the key role in the quality control of silicon materials, have to be well controlled during crystal growth and device fabrication. Co-doping nitrogen (N), germanium (Ge) or carbon (C) into CZ silicon to control defect dynamics and to change defect evolution, so-called “impurity engineering”, has been developed in recent years, and has been widely applied in the fabrication of higher quality CZ silicon used for microelectronics nowadays. This article is to present an overview of the current status of impurity engineering in CZ silicon, based on the co-doping technologies of N, Ge and C. The fundamental properties of these three co-dopants and their interaction with point defects in CZ silicon are firstly introduced. The bulk of the article is focused on the effects of co-dopants on the formation of oxygen precipitates related to internal gettering (IG) of devices for metal contaminants, and voids associated with the gate oxide integrity (GOI) of devices in CZ silicon. Finally, the improvement of CZ silicon mechanical strength by co-doping technology is described.

Attempt to indicate the potential correlation errors of DIC method, the modulation transfer function (MTF) test method is proposed in this paper. An Alumnus plate with random pattern on the surface was moved by a linear stage and commercial DIC software was used to calculate the displacement filed while the reference image was taken at focus and the second image set was taken at different field of view. The calculated displacement fields are corrected with a linear function to eliminate unexpected displacement gradient. Meanwhile, the MTF values are also calculated with the same random pattern images. Finally, the MTF values and the slop coefficient of the linear fitting function are related, by this way, once the camera MTFs are known then the possible displacement error can be estimated.

P-on-n diodes fabricated on n-type Cz Si wafers with different Ge doping concentrations were irradiated with 2MeV electrons and 1MeV equivalent reactor neutrons using a wide range of fluences and examined by combining current and capacitance transient techniques.