Regions Of High Concentration Research Articles

Clustering Methods for the Characterization of Synchrotron Radiation X‐Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque

This study employs computational algorithms to automatically identify and classify features in X‐Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X‐Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X‐Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as Nitrogen oxides (NOx) and Ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Potassium-mediated bacterial chemotactic response.

Bacteria in biofilms secrete potassium ions to attract free swimming cells. However, the basis of chemotaxis to potassium remains poorly understood. Here, using a microfluidic device, we found that Escherichia coli can rapidly accumulate in regions of high potassium concentration on the order of millimoles. Using a bead assay, we measured the dynamic response of individual flagellar motors to stepwise changes in potassium concentration, finding that the response resulted from the chemotaxis signaling pathway. To characterize the chemotactic response to potassium, we measured the dose-response curve and adaptation kinetics via an Förster resonance energy transfer (FRET) assay, finding that the chemotaxis pathway exhibited a sensitive response and fast adaptation to potassium. We further found that the two major chemoreceptors Tar and Tsr respond differently to potassium. Tar receptors exhibit a biphasic response, whereas Tsr receptors respond to potassium as an attractant. These different responses were consistent with the responses of the two receptors to intracellular pH changes. The sensitive response and fast adaptation allow bacteria to sense and localize small changes in potassium concentration. The differential responses of Tar and Tsr receptors to potassium suggest that cells at different growth stages respond differently to potassium and may have different requirements for potassium.

Potassium-mediated bacterial chemotactic response

Bacteria in biofilms secrete potassium ions to attract free swimming cells. However, the basis of chemotaxis to potassium remains poorly understood. Here, using a microfluidic device, we found that Escherichia coli can rapidly accumulate in regions of high potassium concentration on the order of millimoles. Using a bead assay, we measured the dynamic response of individual flagellar motors to stepwise changes in potassium concentration, finding that the response resulted from the chemotaxis signaling pathway. To characterize the chemotactic response to potassium, we measured the dose–response curve and adaptation kinetics via an Förster resonance energy transfer (FRET) assay, finding that the chemotaxis pathway exhibited a sensitive response and fast adaptation to potassium. We further found that the two major chemoreceptors Tar and Tsr respond differently to potassium. Tar receptors exhibit a biphasic response, whereas Tsr receptors respond to potassium as an attractant. These different responses were consistent with the responses of the two receptors to intracellular pH changes. The sensitive response and fast adaptation allow bacteria to sense and localize small changes in potassium concentration. The differential responses of Tar and Tsr receptors to potassium suggest that cells at different growth stages respond differently to potassium and may have different requirements for potassium.

Dimensionless dynamics: Multipeak and envelope solitons in perturbed nonlinear Schrödinger equation with Kerr law nonlinearity

In this work, the dimensionless form of the improved perturbed nonlinear Schrödinger equation with Kerr law of fiber nonlinearity is solved for distinct exact soliton solutions. We examined the multi-wave solitons and rational solitons of the governing equation using the logarithmic transformation and symbolic computation using an ansatz functions approach. Multi-wave solitons in fluid dynamics describe the situation in which a fluid flow shows several different regions (or peaks) of high concentration or intensity of a particular variable (e.g., velocity, pressure, or vorticity). Multi-wave solitons in turbulent flows might indicate the existence of several coherent structures, like eddies or vortices. These formations are areas of concentrated energy or vorticity in the turbulent flow. Understanding how these peaks interact and change is essential to comprehending the energy cascade and dissipation in turbulent systems. Furthermore, a sub-ordinary differential equation approach is used to create solutions for the Weierstrass elliptic function, periodic function, hyperbolic function, Chirped free, dark-bright (envelope solitons), and rational solitons, as well as the Jacobian elliptic function, periodic function, and rational solitons. Also, as the Jacobian elliptic function's' modulus m approaches values of 1 and 0, we find trigonometric function solutions, solitons-like solutions, and computed chirp free-solitons. Envelope solitons can arise in stratified fluids and spread over the interface between layers, such as layers in the ocean with varying densities. Their research aids in the management and prediction of wave events in artificial and natural fluid settings. In fluids, periodic solitons are persistent, confined wave structures that repeat on a regular basis, retaining their form and velocity over extended distances. These structures occur in a variety of settings, including internal waves in stratified fluids, shallow water waves, and even plasma physics.

Effect of composition gradient on detonation initiation in fuel-rich hydrogen-air mixtures in an obstructed channel

Numerical simulations were performed to study the effect of composition gradient and obstacle arrangement on detonation initiation in fuel-rich hydrogen-air mixtures. A third-order WENO method with adaptive mesh refinement (AMR) was used to solve the unsteady, fully-compressible, reactive Navier-Stokes equations coupled to a calibrated chemical-diffusive model (CDM). An obstructed channel at a blockage ratio of 0.3 filled with non-uniform hydrogen-air mixtures of an average H2 concentration 50 vol% was considered. The results show that unilateral obstacle arrangement is more conducive to flame acceleration (FA) and DDT than bilateral obstacle arrangement for both homogeneous and inhomogeneous mixtures. For inhomogeneous mixtures, the flame accelerates faster, and the detonation onset time is shorter when obstacles are placed on the sidewall with a hydrogen concentration closer to the stoichiometric ratio than the those when obstacles are placed on the other sidewall. However, the placement of unilateral obstacles on either the upper or lower sidewall does not significantly affect the DDT run-up distance. Besides, for fuel-rich inhomogeneous hydrogen-air mixtures, detonation tends to be initiated in the regions of high H2 concentration. Idealized models were introduced to simplify the intricate interactions of the reaction waves and flow fields to better understand the connection between H2 concentration distribution and detonation initiation. The analysis suggests that the minimum shock strength required for detonation initiation decreases with increasing equivalence ratio. This is supported by a kinetics analysis with detailed reaction model, which shows that ignition delay increases with decreasing equivalence ratio when detonation is about to be initiated.

A model with multiple intracranial aneurysms: possible hemodynamic mechanisms of aneurysmal initiation, rupture and recurrence

BackgroundHemodynamic factors play an important role in aneurysm initiation, growth, rupture, and recurrence, while the mechanism of the hemodynamic characteristics is still controversial. A unique model of multiple aneurysms (initiation, growth, rupture, and recurrence) is helpful to avoids the confounders and further explore the possible hemodynamic mechanisms of aneurysm in different states.MethodsWe present a model with multiple aneurysms, and including the states of initiation, growth, rupture, and recurrence, discuss the proposed mechanisms, and describe computational fluid dynamic model that was used to evaluate the likely hemodynamic effect of different states of the aneurysms.ResultsThe hemodynamic analysis suggests that high flow impingement and high WSS distribution at normal parent artery was found before aneurysmal initiation. The WSS distribution and flow velocity were decreased in the new sac after aneurysmal growth. Low WSS was the risk hemodynamic factor for aneurysmal rupture. High flow concentration region on the neck plane after coil embolization still marked in recanalized aneurysm.ConclusionsAssociations have been identified between high flow impingement and aneurysm recanalization, while low WSS is linked to the rupture of aneurysms. High flow concentration and high WSS distribution at normal artery associated with aneurysm initiation and growth, while after growth, the high-risk hemodynamics of aneurysm rupture was occurred, which is low WSS at aneurysm dome.

Experimental confirmation of the formation of collective modes of the magnetization motion of paramagnetic particles in dilute solutions due to spin exchange

Experimental confirmation of the manifestations of new spin exchange paradigm in EPR spectra of 14N nitroxide radical solutions is presented. It was shown that in the region of relatively low concentrations of radicals, the two side components of the spectrum have a mixed shape (the sum of the absorptive line and dispersive line). The dispersion contributions in these two lines have opposite signs. As the concentration of radicals increases, the contribution of dispersion passes through an extremum and in the region of maximum contribution of dispersion, the contribution of absorption to these two lines changes sign. In the region of high concentrations of radicals, when one homogeneously broadened line is practically observed, it turns out that these side components have resonant frequencies that do not coincide with the frequency of the center of gravity of the spectrum.

Application of Deep Learning on Structure Displacement Measurement and Accuracy Analysis

Displacement serves as a crucial indicator in structural health monitoring within civil engineering, with computer vision being a commonly employed tool due to its cost-effectiveness and convenience. This paper introduces a methodology for tracking feature points computed via computer vision techniques SuperPoint and SuperGlue. Two test scenarios are conducted: a bridge loading test characterized by a low uniform stress state, and a tensile strength test marked by a high stress state. However, a notable drawback of this method is the potential misalignment between the survey point (the point under measurement) and the feature point, leading the errors. To address this issue, a fundamental principle for selecting feature points is presented. In the bridge loading test, the displacement of the survey point is recorded, and the influence of misalignment between survey point and feature point on accuracy is analyzed under low-stress uniform conditions. The findings demonstrate the feasibility of this method for field measurements in civil engineering. Subsequently, a tensile strength test is conducted to assess the influence of different feature point selection criteria on accuracy under high-stress conditions. Concurrently, the DIC software Ncorr is applied to analyze the displacement field of feature points near the survey point. The results highlight the significant influence of selecting feature points within regions of high stress concentration on measurement accuracy.

Effect of hydrogen ratio on leakage and explosion characteristics of hydrogen-blended natural gas in utility tunnels

Based on computational fluid dynamics technology, the effect of hydrogen blending ratio (HBR) on the concentration distribution of leaked gas diffusion in utility tunnels was investigated, and based on this, the explosion characteristics of hydrogen-blended natural gas (HBNG) were studied in an inhomogeneous concentration field. The results showed that the concentration field of HBNG gradually stabilized in the downstream of the leak site, and the peak concentration increased linearly with HBR. When HBR≤5%, a high concentration region above the upper explosive limit is formed inside the gas cloud. This characteristic of the distribution induces secondary combustion in utility tunnels. The maximum flame velocity and steady acceleration distance increase with the increase of HBR. The flame shape is significantly affected by the concentration field distribution. The explosion overpressure induced by the real concentration field created multiple peak structures that exacerbated the explosion disaster complexity. The first overpressure peak at the ends was formed by the superposition of the precursor shock wave and its reflected wave, and those at other locations are caused by the shock waves. However, all maximum overpressure peaks are formed by the superposition of multiple reflections of the explosion wave. The maximum peak overpressure at both ends of the tunnel is higher than that in the middle. The explosion overpressure induced by the inhomogeneous gas cloud is lower than that caused by homogeneous gas cloud under the same volume. The research results can provide an effective reference for the anti-explosion design and risk assessment of utility tunnel.

Insight into the Structural and Dynamical Processes of Peptides by Means of Vibrational and Ultrasonic Relaxation Spectroscopies, Molecular Docking, and Density Functional Theory Calculations

We report a detailed investigation of the vibrational modes, structure, and dynamics of glutathione (GSH) solutions using ultrasonic relaxation spectroscopy, FT-IR vibrational spectroscopy, and electronic absorption measurements. The experimental data were analyzed using density functional theory (DFT) and molecular docking calculations. Three distinct Debye-type relaxation processes can be observed in the acoustic spectra, which are assigned to conformational changes between GSH conformers, the self-association of GSH, and protonation processes. The standard volume changes for each process were estimated both experimentally and theoretically, revealing a close resemblance among them. The higher the effect of the relaxation process in the structure, the greater the induced volume changes. From the temperature dependence of specific acoustic parameters, the thermodynamic characteristics of each process were determined. The experimental FT-IR spectra were compared with the corresponding theoretically predicted vibrational spectra, revealing that the GSH dimers and extended conformers dominate the structure of GSH solutions in the high-concentration region. The absorption spectra in the ultraviolet region confirmed the gradual aggregation mechanism that takes place in the aqueous GSH solutions. The results of the present study were discussed and analyzed in the framework of the current phenomenological status of the field.

Electrokinetic ion transport of viscoelastic fluids in a pH-regulated nanochannel

The objects of nanofluidics-based biological detection are usually viscoelastic fluids, and the rheological properties of viscoelastic fluids can have complex effects on electroosmotic flow and electrokinetic ion transport in nanochannels. However, researchers have mainly focused on Newtonian fluids. In this study, numerical simulations were performed on the electrokinetic ion transport of viscoelastic fluids in a pH-regulated nanochannel. The Oldroyd-B model is used to describe the behavior of the polyacrylamide (PAM) solution, and the Poisson-Nernst-Planck model is used to describe the potential and ion concentration in the electrolyte solution. Compared with the Newtonian fluid, at pH = 8, the PAM solution of cp = 500 ppm has a larger region of low ion concentration in front of the nanochannel entrance and has a larger region of high ion concentration behind the nanochannel exit. When the pH is increased to 8, the surface charge density |σw| increases sharply, which is about 5 times of that at pH = 6. At pH = 8, cKCl = 20 mM, and cp = 500 ppm, two distinct vortices appear behind the nanochannel exit, and rotate in opposite directions.

Optimizing collective behavior of communicating active particles with machine learning

Bacteria and other self-propelling microorganisms produce and respond to signaling molecules to communicate with each other (quorum sensing) and to direct their collective behavior. Here, we explore agents (active particles) which communicate with each other to coordinate their collective dynamics for maximizing nutrient consumption. Using reinforcement learning and neural networks, we identify three different strategies: a ‘clustering strategy’, where the agents accumulate in regions of high nutrient concentration; a ‘spreading strategy’, where particles stay away from each other to avoid competing for sparse resources; and an ‘adaptive strategy’, where the agents adaptively decide to either follow or stay away from others. Our work exemplifies the idea that machine learning can be used to determine parameters that are evolutionarily optimized in biological systems but often occur as unknown parameters in mathematical models describing their dynamics.

Study on motion behaviour of coal water slurry particles under vibration conditions

This study uses computational fluid dynamics (CFD) to simulate the motion behavior of coal-water slurry (CWS) particles under vibration conditions. Results show that without vibration, the coal particles settle to the bottom of the container due to gravity, forming distinct regions of low, transition, and high concentration. Under low vibration intensity, the slight energy activates particle motion and promotes settlement. Under high vibration intensity, violent reciprocating motion causes severe shaking and uncertainty, resulting in non-uniform particle distribution and back mixing. Particle velocity distribution is significantly influenced by vibration, with higher frequencies and amplitudes resulting in greater velocities. Additionally, particle pseudo-temperature is higher in the near-wall area due to collisions with particles and wall surfaces. Low frequency and amplitude promote particle settlement, increasing compactness in the bottom area and reducing concentration in the top area. The findings provide valuable insights into CWS behavior under vibration conditions, which can be used to optimize the design and operation of CWS handling and transportation systems.

Mesoporous γ-AlOOH as an adsorbent for silica removal from aqueous solutions

Silica plays a crucial role in the formation of inorganic scaling. Therefore, we present a study on the effectiveness of γ-AlOOH (boehmite) as a silica removal adsorbent. Kinetic analysis reveals a combination of chemical and physical adsorption mechanisms, with the pseudo-second-order model suggesting significant chemisorption. The adsorption isotherm of silica followed Freundlich model in higher concentration region at over 50 mg/L and mesoporous effectively act on silica adsorption in tap water. This study proposes γ-AlOOH as a novel adsorbent to address issues related to silica in various industries.

Capillary imbibition of confined monodisperse emulsions in microfluidic channels.

We study imbibition of a monodisperse emulsion into high-aspect ratio microfluidic channels with the height h comparable to the droplet diameter d. Two distinct regimes are identified in the imbibition dynamics. In a strongly confined system (the confinement ratio d/h = 1.2 in our experiments), the droplets are flattened between the channel walls and move more slowly compared to the average suspension velocity. As a result, a droplet-free region forms behind the meniscus (separated from the suspension region by a sharp concentration front) and the suspension exhibits strong droplet-density and velocity fluctuations. In a weaker confinement, d/h = 0.65, approximately spherical droplets move faster than the average suspension flow, causing development of a dynamically unstable high-concentration region near the meniscus. This instability results in the formation of dense droplet clusters, which migrate downstream relative to the average suspension flow, thus affecting the entire suspension dynamics. We explain the observed phenomena using linear transport equations for the particle-phase and suspension fluxes driven by the local pressure gradient. We also use a dipolar particle interaction model to numerically simulate the imbibition dynamics. The observed large velocity fluctuations in strongly confined systems are elucidated in terms of migration of self-assembled particle chains with highly anisotropic mobility.

Non-Newtonian Dynamics in Water-in-Salt Electrolytes.

Water-in-salt electrolytes have attracted considerable interest in the past decade for advanced lithium-ion batteries, possessing important advantages over the non-aqueous electrolytes currently in use. A battery with a LiTFSI-water electrolyte was demonstrated in which an operating window of 3 V is made possible by a solid-electrolyte interface. Viscosity is an important property for such electrolytes, because high viscosity is normally associated with low ionic conductivity. Here, we investigate shear and longitudinal viscosities using shear stress and compressional longitudinal stress measurements as functions of frequency and concentration. We find that both viscosities are frequency-dependent and exhibit almost identical frequency and concentration dependences in the high-concentration region. A comparison to quasielastic neutron scattering experiments suggests that both are governed by structural relaxation of the TFSI- network. Thus, LiFTSI-water electrolytes appear to be an unusual case of a non-Newtonian fluid, where shear and longitudinal viscosities are determined by the same relaxation mechanism.

Mesoporous Silica Engineered Lithium Metal Batteries: Unraveling the Mechanisms behind Dendrite Inhibition and Enhanced Electrochemical Performance

Lithium metal batteries are widely considered to be a next-generation battery technology due to their high energy density, fast charging capability, and potential to power a wide range of applications, from portable electronics to electric vehicles.Compared to conventional lithium-ion batteries, which use graphite as the anode material, lithium metal batteries have the potential to offer significantly higher energy density. This is because lithium metal has a much higher theoretical capacity than graphite, meaning it can store more energy per unit weight or volume. As a result, lithium metal batteries could potentially offer longer run times and smaller form factors than lithium-ion batteries, which are already widely used in consumer electronics and electric vehicles.In addition to their high energy density, lithium metal batteries also offer fast charging capabilities. This is because the lithium metal anode can be charged more quickly than the graphite anode used in conventional lithium-ion batteries. This could enable faster charging times for electric vehicles and other applications, reducing the time required for recharging and improving their overall usability.However, one of the major challenges with lithium metal batteries is dendrite growth, which can occur when lithium metal ions deposit unevenly on the surface of the anode during charging. These dendrites are needle-like structures that can penetrate the separator and cause short-circuits, leading to reduced battery performance and safety issues. As a result, much of the research on lithium metal batteries has focused on developing strategies to prevent dendrite growth, such as using coatings or electrolytes with additives that can stabilize the lithium metal anode.Despite these challenges, lithium metal batteries continue to be a promising next-generation battery technology. Researchers are working on improving their energy density, charging speed, and safety, and they are exploring a range of applications, from portable electronics to electric vehicles and grid-scale energy storage. If these challenges can be overcome, lithium metal batteries could revolutionize the energy storage industry and enable a wide range of new and exciting applications.To address this issue, we have explored the use of various coatings on the lithium metal anode to prevent dendrite growth. In this study, ordered mesoporous silica with different pore sizes and pore structures, such as SBA-15 and KIT-6, were used as coatings between the lithium metal anode and separator.SBA-15 and KIT-6 are two types of mesoporous silica materials that are widely used in various applications due to their unique properties. The synthesis of these materials involves the use of templates to control the pore size and structure.They are synthesized using a combination of a surfactant(pluronic P123 or cetyltrimethylammonium bromide;CTAB) as the template and tetraethyl orthosilicate(TEOS) as the silica source. The synthesis involves mixing a solution of surfactant and TEOS in acidic conditions, followed by aging and calcination steps. The resulting materials have a highly ordered hexagonal pore structure(SBA-15) and body-centered cubic pore structure(KIT-6) with a pore size ranging from 5 to 20 nm.By coating the surface of the lithium metal with a uniform pore structure material such as ordered mesoporous silica, dendrite growth can be suppressed and lithium deposition can occur more uniformly.The uniform pore structure material acts as a physical barrier to lithium ion diffusion and helps to control the deposition of lithium ions onto the surface of the lithium metal. When lithium ions are deposited onto the surface of the lithium metal, they can form a layer of lithium that can grow and become thick over time. If the lithium layer is too thick, it can become unstable and form dendrites. However, the uniform pore structure material can help to limit the thickness of the lithium layer and promote the deposition of lithium ions in a more uniform manner, thus reducing the likelihood of dendrite formation.Additionally, the uniform pore structure material can help to reduce the concentration of lithium ions at the surface of the lithium metal, which is another factor that can contribute to dendrite growth. By limiting the concentration of lithium ions at the surface, the uniform pore structure material can help to prevent the formation of high-concentration regions that can promote dendrite growth.Overall, the mechanism by which a uniform pore structure material can suppress dendrite growth involves controlling the deposition of lithium ions onto the surface of the lithium metal, limiting the thickness of the lithium layer, and reducing the concentration of lithium ions at the surface. These factors help to promote more uniform lithium deposition and prevent the formation of dendrites, leading to improved battery performance and safety.

Denoising diffusion model as handwritten digit generator

The diffusion model is the process by which variables are introduced and propagated through a population. The equation can be applied to any physical system that exhibits such processes through time. Generally speaking, diffusion occurs when a physical system is connected to another by a small number of interconnected points; the interconnected points' overall connectivity can be represented by a network. The diffusion model is a mathematically defined process used to analyse the movement of particles from a region of high concentration to that of low concentration. Based on the diffusion coefficient and polarization, several studies have established that the equilibrium port-to-port distance can be calculated. The diffusion model is useful for solving the problem of noise in imaging systems, especially when an object has similar properties in all directions. When discussing diffusion, it is essential to refer to the diffusion coefficient. The literatures find denoising diffusion model to involves the process where a pixel value is estimated based on values at surrounding pixels. On the other hand, a forward process is passing through an image and replacing pixels based on their quality estimates. Reconstruction involves reconstructing an image from its components, including the subsamples and low-quality components. This model achieves satisfactory performance on digital number image generation.

Concentration gradient inversion via sedimentation of coffee grounds in full immersion brewing

In full immersion brewing methods, the coffee grounds are fully submerged in water during the brew and then filtered out prior to consumption. If the brew is not agitated, most of the coffee grounds quickly float to the surface, but then eventually sediment to the bottom as they de-gas and become less buoyant. Although this behavior is readily observed, little research has investigated the impact of the transient sedimentation process on the concentration profile of total dissolved solids (TDS) within the brew. Here, we report that unagitated full immersion brews undergo a concentration gradient inversion midway through the brewing process and that the time scale of the inversion depends sensitively on the roast level of the coffee grounds. Systematic measurements of the TDS versus both depth and time reveal that the brew contains a region of nearly twice the concentration near the top air/liquid interface at the beginning of the brew, but then the higher concentration region inverts to the bottom of the container as the brew progresses. Time-lapse photography observations indicate that the time scale of the gradient inversion is consistent with the average rate of sedimentation, which occurs up to a factor of three slower for darker roasts. A key implication of our results is that sensory evaluations obtained via the traditional “cupping” method used widely in the coffee industry might be confounded by differences in TDS for different roast levels.