The comminution of spent lithium-ion batteries (LIBs) produces a powder containing the active cell components, commonly referred to as “black mass.” Recently, froth flotation has been proposed to treat the fine fraction of black mass (<100 µm) as a method to separate anodic graphite particles from cathodic lithium metal oxides (LMOs). So far, pyrolysis has been considered as an effective treatment to remove organic binders in the black mass in preparation for flotation separation. In this work, the flotation performance of a pyrolyzed black mass obtained from an industrial recycling plant was improved by adding a pre-treatment step consisting of mechanical attrition with and without kerosene addition. The LMO recovery in the underflow product increased from 70% to 85% and the graphite recovery remained similar, around 86% recovery in the overflow product. To understand the flotation behavior, the spent black mass from pyrolyzed LIBs was compared to a model black mass, comprising fully liberated LMOs and graphite particles. In addition, ultrafine hydrophilic particles were added to the flotation feed as an entrainment tracer, showing that the LMO recovery in overflow products is a combination of entrainment and true flotation mechanisms. This study highlights that adding kerosene during attrition enhances the emulsification of kerosene, simultaneously increasing its (partial) spread on the LMOs, graphite, and residual binder, with a subsequent reduction in selectivity.
The Frumkin or Fowler–Guggenheim isotherm, which has three fitting parameters, is often converted to a linear form with two fitting parameters to facilitate parameter estimation by linear regression. This conversion is made possible by way of replacing the unknown capacity parameter of the Frumkin isotherm with a surrogate value. The capacity parameters of certain simple isotherms (e.g., Dubinin–Radushkevich) are often used as surrogates. However, such surrogates have never been evaluated for validity or accuracy. In this paper, the three-parameter Frumkin isotherm was fit to previously published isotherm data to identify all three unknown parameters, including the capacity parameter. In the cases examined, the fitted capacity values were found to differ rather significantly from the surrogate capacity values used in the two-parameter Frumkin isotherm. The dubious practice of transforming the three-parameter Frumkin isotherm into the two-parameter Frumkin isotherm for linear estimation of parameters should be discarded in favor of estimating all three parameters by nonlinear regression.
Transforming carbon dioxide into valuable chemicals and fuels, is a promising tool for environmental and industrial purposes. Here, we present catalysts comprising of cobalt (oxide) nanoparticles stabilized on various support oxides for hydrocarbon production from carbon dioxide. We demonstrate that the activity and selectivity can be tuned by selection of the support oxide and cobalt oxidation state. Modulated excitation (ME) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) reveals that cobalt oxide catalysts follows the hydrogen-assisted pathway, whereas metallic cobalt catalysts mainly follows the direct dissociation pathway. Contrary to the commonly considered metallic active phase of cobalt-based catalysts, cobalt oxide on titania support is the most active catalyst in this study and produces 11% C2+ hydrocarbons. The C2+ selectivity increases to 39% (yielding 104 mmol h−1 gcat−1 C2+ hydrocarbons) upon co-feeding CO and CO2 at a ratio of 1:2 at 250 °C and 20 bar, thus outperforming the majority of typical cobalt-based catalysts.
Breakthrough curves of water contaminants are usually analyzed using simple fixed bed models such as the Bohart-Adams, Thomas, and Yoon-Nelson equations, which are by design symmetric. Because breakthrough data often follow an asymmetric pattern, the use of models that do not account for asymmetry could lead to poor fits, consequently resulting in erroneous estimates of breakthrough and exhaustion times. To address this issue, the Bohart-Adams, Thomas, and Yoon-Nelson models were modified by a logarithmic transformation to enhance their data fitting ability. The three modified models were found capable of providing robust fits to seven separate sets of previously reported asymmetric breakthrough data of water contaminants (fluoride, methylene blue, salicylic acid, lead, mercury, nickel, and arsenic), with reported residual root mean square error (RRMSE) values ranging from 0.019 to 0.046. In consequence, the new models were found capable of providing reliable estimates of breakthrough and exhaustion times corresponding to any predetermined concentration level. By contrast, the three original models were found to perform poorly, reporting inferior RRMSE values ranging from 0.038 to 0.086 for data fits and providing grossly inaccurate estimates of breakthrough and exhaustion times. The new models contain only parameters that appear in the original models, and are highly flexible, being able to assume virtually all monotonically increasing sigmoid shapes. They represent a far more accurate alternative to the original models.
Field-controlled micro–nano manipulations and micro–nano robots have attracted increasing attention in the fields of medicine, environment, engineering, and energy due to their outstanding characteristics which include small size, strong controllability, cluster action, and strong penetrability; thus, they have gradually become an important research focus in micro–nano manufacturing and in vivo detection. However, precise cluster control, targeted drug delivery in vivo, and cellular micro–nano operation remain challenges. Herein, the scientific research results produced in recent years to meet these challenges are studied. Considering the current research enthusiasm and application challenges, the micro–nano manipulations and micro–nano robots driven by physical fields (magnetic field, sound field, and light field) are mainly discussed. This review includes detailed analysis of control mechanism, control objectives, and supporting technologies; analysis of recent research results, and advantages and future development trends driven by physic fields, etc. This review involves the crossover and integration of multiple disciplines (including microelectronic technology, micro–nano processing technology, biology, physics, chemistry, machinery, and automation, etc.), hoping to inspire relevant practitioners to create new research perspectives, and promote the development of micro–nano robotics.
Semiconductor-superconductor hybrids are commonly used in research on topological quantum computation. Traditionally, top-down approaches involving dry or wet etching are used to define the device geometry. These often aggressive processes risk causing damage to material surfaces, giving rise to scattering sites particularly problematic for quantum applications. Here, a method that maintains the flexibility and scalability of selective area grown nanowire networks while omitting the necessity of etching to create hybrid segments is proposed. Instead, it takes advantage of directional growth methods and uses bottom-up grown indium phosphide (InP) structures as shadowing objects to obtain selective metal deposition. The ability to lithographically define the position and area of these objects and to grow a predefined height ensures precise control of the shadowed region. The approach by growing indium antimonide nanowire networks with well-defined aluminium and lead (Pb) islands is demonstrated. Cross-section cuts of the nanowires reveal a sharp, oxide-free interface between semiconductor and superconductor. By growing InP structures on both sides of in-plane nanowires, a combination of platinum and Pb can independently be shadow deposited, enabling a scalable and reproducible in situ device fabrication. The semiconductor-superconductor nanostructures resulting from this approach are at the forefront of material development for Majorana based experiments.
Blended electrode materials containing high-capacity silicon (Si) and robust graphite (Gr) materials are considered advanced alternatives to pure graphite electrodes used in Li-ion batteries. Understanding the component-specific lithiation and delithiation behavior and electrochemical interactions between the blended materials is of crucial importance for targeted optimization of composition and microstructural design, yet hardly addressed to date. Herein, a model-like Si/Gr blended electrode and special electrochemical cell are introduced to directly capture the component specific behaviors for the first time. This includes studies of the formation cycles, the reaction distribution between Si and Gr, the component-specific contributions to the capacity at different charge and discharge rates, and the internal dynamics during pulse loads and subsequent relaxation. The deconvolution of the components’ behavior during operation provides fundamental insights that contribute to a profound understanding and targeted optimization of Si/Gr blended electrodes. Furthermore, the application of the presented experimental approach can serve scientists to identify and study other advanced materials combinations as blended electrodes for rechargeable batteries.
A new simultaneous dopant, antireflection, and surface passivation scheme based on sulfonated polytetrafluoroethylene (Nafion) is combined with the state of the art in research grade carbon nanotube–...
The emergence of nanotechnology has created unprecedented hopes for addressing several unmet industrial and clinical issues, including the growing threat of so-termed “antibiotic resistance” in medicine. Over the last decade, nanotechnologies have demonstrated promising applications in the identification, discrimination, and removal of a wide range of pathogens. Here, recent insights into the field of bacterial nanotechnology are examined that can substantially improve the fundamental understanding of nanoparticle and bacteria interactions. A wide range of developed nanotechnology-based approaches for bacterial detection and removal together with biofilm eradication are summarized. The challenging effects of nanotechnologies beneficial bacteria in the human body and environment and the mechanisms of bacterial resistance to nanotherapeutics are also reviewed.
Fixed bed adsorption of toxic metal ions such as chromium is a research area of current interest. Mathematical models are routinely used to summarize breakthrough results of metal ions, which often display varying degrees of curve asymmetry. This work introduces the Weibull function as a simple model for correlating asymmetric breakthrough curves of chromium. The Weibull function is similar to the widely used Bohart-Adams model in several aspects. For example, they both produce sigmoid or S-shaped curves. Their simple mathematical forms can be linearized and linear regression can then be used to estimate their parameters. However, the Weibull function, unlike the Bohart-Adams model, can track the trajectory of asymmetric breakthrough data. Applying the Weibull function to published breakthrough data of chromium, this article illustrates its outright superiority versus the Bohart-Adams model in representing highly asymmetric data. Both equations provide satisfactory fits to breakthrough data exhibiting a moderate degree of curve asymmetry.