Single Stream Research Articles

Batch to Continuous: From Laboratory Recycle Trickle Bed Test Reactor Data to Full-Scale Plant Preliminary Design—A Case Study Based on the Hydrogenation of Resorcinol

The fine chemical and pharmaceutical sectors are starting to advocate for the use of flow chemistry due to reasons such as the environment, health and safety, efficiency, cost saving, and regulatory compliance. The use of a trickle bed or fixed bed system could replace a batch autoclave typically used for hydrogenation reactions. However, there are few studies that detail the process from laboratory proof of concept through design to commercial realization. This study, using the production of 1,3-cyclohexanedione from the catalytic hydrogenation of resorcinol as a case study, demonstrates how the laboratory-scale recycle trickle bed can be used for catalyst screening and selection. Further, design data are generated by operation over a range of design superficial velocities and operating pressures that are used to derive a design correlation that is then used to specify a single stream plant at a level of definition consistent with a Preliminary Design for capital cost estimation. Finally, the further actions required in terms of data generation to increase the level of definition and confidence to a sanction grade or final design are discussed.

Nektar++: Development of the compressible flow solver for jet aeroacoustics

A recently developed computational framework for jet noise predictions is presented. The framework consists of two main components, focusing on source prediction and noise propagation. To compute the noise sources, the turbulent jet is simulated using the compressible flow solver implemented in the open-source spectral/hp element framework Nektar++, which solves the unfiltered Navier-Stokes equations on unstructured grids using the high-order discontinuous Galerkin method. This allows high-order accuracy to be achieved on unstructured grids, which in turn is important in order to accurately simulate industrially relevant geometries. For noise propagation, the Ffowcs Williams - Hawkings method is used to propagate the noise between the jet and the far-field. The paper provides a detailed description of the computational framework, including how the different components fit together and how to use them. To demonstrate the framework, two configurations of a single stream subsonic jet are considered. In the first configuration, the jet is treated in isolation, whereas in the second configuration, it is installed under a wing. The aerodynamic results for these two jets show strong agreement with experimental data, while some discrepancies are observed in the acoustic results, which are discussed. In addition to this, we demonstrate close to linear scaling beyond 100,000 processors on the ARCHER2 supercomputer.

State of the Art of Modelling and Design Approaches for Ejectors in Proton Exchange Membrane Fuel Cell

Proton exchange membrane fuel cell (PEMFC) has a promising future in the power generation and transportation fields. Recirculation of unused anodic gases is fundamental to achieve a high-performance energy system, and this is usually attained employing ejectors or pumps. With respect to the latter, ejectors present no moving parts, thus resulting in both higher overall efficiency of the system and lower maintenance cost. Their main drawback is represented by the narrow optimal operative range: the entrainment ratio (ER) greatly depends on primary pressure, working pressure, and operative condition in general. In the last decade, numerous authors focused their efforts on fully comprehending and correctly simulating their working principles and analyzing how geometrical parameters influence ER and design different geometries to enlarge the operative range. The aim of this paper is to present in an ordered and clear manner the state of the art of ejector design, both from simulative (turbulence model, single or multiphase stream, etc.) and empirical (commonly used “rule of thumb”) points of view.

Rozwiązanie problemu tworzenia się zatorów w rurociągach podczas transportu gazu poprzez proces automatycznego strumieniowania

The article presents the application of autoejection process to ensure efficient operation of pipeline systems and gas separation installations when it is impossible to remove and utilize the condensate from the pipelines. Thus, in the suggested ejector, a single common stream is separated into two streams, i.e., an active and a passive stream, as opposed to two separate independent streams in existing ejection devices. As a result, we refer to such an ejection process as autoejection, or a self-ejection process. Such a procedure is run in the pipeline with the goal of blowing and dusting the liquid phase through the central high-velocity nozzle in circumstances of mass blocking and coating rather than expelling the liquid from the pipes. On the cross-sectional area of the belts, the velocity profiles of flows in laminar and turbulent regimes are known to differ greatly from one another. The adhesion forces acting from the tube's central axis outwards, towards its walls cause the flow rate to decrease. There is a cross-sectional interaction of flows in this mode, according to experimental studies of turbulent flows. As a result, compared to the laminar domain, the flow velocities in this regime are more equally distributed across the cross-section of the flow, and their values are roughly equal to the flow's average value. In this case, based on the dependences of the flow ∆p = f(W), it is known from the calculations and the table that at such velocity limits of the flows, the “braking” pressure (p0) of the liquid coating on the pipe walls corresponds to the maximum velocity of the central gas flow. The autoejection process can occur due to the difference between the static pressures. Unsaturated absorbent coatings can be blasted off the tube absorber's walls using this technique and blended back into the main gas stream. Gas-liquid autoejectors installed along the pipe absorber's length make it possible to use this method. The purpose and principles of autoejectors’ operation are considered and a perspective of their application in tube absorbers is noted.

Value Addition of Recyclable Solid Waste Materials: The Case of Namibia

Solid waste recycling is an important component of sustainable economic development. Research indicates that the growing need for raw materials and the difficulties associated with managing solid waste have led to the development of recycling as a rational strategy for advancing sustainability in the twenty-first century. While value-addition processes are highlighted in various research on solid waste management carried out in Namibia and other parts of southern Africa the focus has been on single waste streams, actors industry, waste reduction options or recommendations on recycling processing systems. This paper aims to highlight value chains of a variety of recyclable materials in the Namibian recycling industry an endeavour which may assist policymakers in understanding how the industry can contribute to sustainable industrial growth and employment creation. The study was a descriptive case study design, which was qualitative. Results indicate that in Namibia recycling industry processes a wide range of recyclable products such as plastic, paper and glass bottles. Except for plastic, the value addition of recovered materials was limited to the pre-processing stage. The full growth of the industry is still to be realised. Policy and intervention programs should promote more value addition in raw materials in the country as a whole considering the benefits to sustainable economic growth.

Underwater image enhancement using Divide-and-Conquer network.

Underwater image enhancement has become the requirement for more people to have a better visual experience or to extract information. However, underwater images often suffer from the mixture of color distortion and blurred quality degradation due to the external environment (light attenuation, background noise and the type of water). To solve the above problem, we design a Divide-and-Conquer network (DC-net) for enhancing underwater image, which mainly consists of a texture network, a color network and a refinement network. Specifically, the multi-axis attention block is presented in the texture network, which combine different region/channel features into a single stream structure. And the color network employs an adaptive 3D look-up table method to obtain the color enhanced results. Meanwhile, the refinement network is presented to focus on image features of ground truth. Compared to state-of-the-art (SOTA) underwater image enhance methods, our proposed method can obtain the better visual quality of underwater images and better qualitative and quantitative performance. The code is publicly available at https://github.com/zhengshijian1993/DC-Net.

Over- and underweighting of extreme values in decisions from sequential samples.

People routinely make decisions based on samples of numerical values. A common conclusion from the literature in psychophysics and behavioral economics is that observers subjectively compress magnitudes, such that extreme values have less sway over people's decisions than prescribed by a normative model (underweighting). However, recent studies have reported evidence for anti-compression, that is, the relative overweighting of extreme values. Here, we investigate potential reasons for this discrepancy in findings and propose that it might reflect adaptive responses to different task requirements. We performed a large-scale study (n = 586) of sequential numerical integration, manipulating (a) the task requirement (averaging a single stream or comparing two interleaved streams of numbers), (b) the distribution of sample values (uniform or Gaussian), and (c) their range (1-9 or 100-900). The data showed compression of subjective values in the averaging task, but anticompression in the comparison task. This pattern held for both distribution types and for both ranges. In model simulations, we show that either compression or anticompression can be beneficial for noisy observers, depending on the sample-level processing demands imposed by the task. This suggests that the empirically observed patterns of over- and underweighting might reflect adaptive responses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A comprehensive assessment and comparison of the impacts of storage parameters on solute transport in streams using a novel framework

Researchers must determine the storage parameter values to understand the storage processes and compare them across streams. These parameters are highly variable in time and space, making them challenging to measure. Individual studies of a single stream cannot characterize the interrelations among the processes involved in solute transport, hydraulic variables, and physical stream characteristics. Despite decades of research, a comprehensive framework is lacking. This study uses a simple and effective Monte Carlo approach to propose a general framework within which most solute transport models can undergo sensitivity analysis. This framework provides a comprehensive depiction of model performance under various scenarios, such as flow properties, stream geomorphology, and solute status. The relative and absolute sensitivities of three solute transport models, namely, the modified advection–dispersion equation (MADE), transient storage model (TSM), and variable residence time (VART), were evaluated and compared within the proposed framework. The main findings can be summarized as follows. In most conditions, storage models are more sensitive to the ratio of the storage zone cross-section to the stream cross-section than other parameters. Increasing the exchange rate between the stream and storage zones reduces the sensitivity to its indicative parameter and, consequently, its impact on the concentration time series. MADE's capability to identify modification parameters is higher for streams where dispersion dominates over advection, and their estimates have lower uncertainty. On the other hand, as advection becomes more dominant over dispersion in TSM and VART, estimated storage parameters have lower uncertainty.“

Climate-driven differences in flow regimes alter tropical freshwater ecosystems with consequences for an endemic shrimp

Climate-driven shifts in the natural flow regime can threaten species persistence in stream systems, and anticipating such shifts before they occur is critical for conservation. We can explore how climate change may impact biota by examining natural systems that differ in terms of climate yet are similar in terms of other landscape features such as geology, size, and elevation. Across an established precipitation and hydrologic gradient on the east coast of Hawaii Island, we sampled stream habitat and populations of the endemic migratory mountain shrimp Atyoida bisulcata over three years and examined how habitat as well as population metrics and individual condition respond to differences in stream flow. Along the precipitation gradient, baseflow declined and streams shifted from run/riffle systems with moss cover to those with predominately pools and limited available habitat. Across years, baseflow conditions were relatively consistent within streams while measures of stream flow stability and the duration of high flows were more variable. Streams with high and persistent baseflow had greater atyid biomass density with larger individuals less prone to disease. Within-stream interannual variation in baseflow was low relative to differences across streams, and most A. bisulcata metrics also had low within-stream interannual variability, making average baseflow an appropriate surrogate for differences in suitability. However, extremes in annual rainfall may result in high variability in A. bisulcata metrics within a single stream due to seasonal drying or persistent high flows, highlighting the importance of long-term monitoring to fully understand population responses to climate-mediated stream flow. Our study suggests that changes in rainfall patterns will alter stream flow and may ultimately negatively influence tropical stream organisms.

Smart-sight: Video-based waste characterization for RDF-3 production

A material recovery facility (MRF) can transform municipal solid waste (MSW) into a valued commodity called refuse-derived fuel (RDF) as a promising solution to waste-to-energy conversion. The quality of the produced RDF significantly relies on the composition of in-feed waste and waste characterization method applied for auditing purposes, a process that is both time-consuming and fraught with potential hazards. This study focuses to enhance the workflow of the waste characterization process at an MRF. A solution named Smart Sight is proposed to detect and classify waste based on videos recorded after processing MSW through a mechanical sorting line consisting of bag breakers and trommel screens. A comprehensive dataset is created encompassing thirteen mixed waste classes from single and multi-family streams. The dataset is preprocessed with motion compensation techniques and frame differencing methods to extract and refine valuable frames. A one-stage YOLO detector model is then trained over the dataset. The experimental results show that the proposed method works efficiently at detecting and classifying waste objects in indoor MRF environments. Accuracy, precision, recall, and F1 score related to the proposed solution are found to be 0.70, 0.762, 0.69 and 0.72, respectively, with a mAP@0.5 of 0.716. The proposed approach is validated using data collected from local MRF by comparing the estimated waste composition values of the proposed solution with laboratory results obtained through current standardized industrial practices. Comparison reveals that waste characterization estimation obtained is consistent with the laboratory results, inferring that Smart-Sight is a viable tool for estimating waste composition.

Multimodal measurements enhance insights into emotional responses to immediate feedback.

Adaptive learning technologies often provide students with immediate feedback on task performance. This feedback can elicit various emotional responses, which, in turn, influence learning. Most recent studies capture these emotions by single data streams, contradicting the multi-componential nature of emotion. Therefore, this study investigated 32 university students solving mathematical problems using an adaptive learning technology. Students received immediate feedback on every step in the solution process, after which their physiological, experiential and behavioral responses to this feedback were recorded. Physiological arousal was measured by electrodermal activity, valence was measured by self-reports (experiential), and emotion types were measured by observations of facial expressions (behavioral). Results showed more peaks in electrodermal activity after feedback than was expected based on chance. These responses were comparable in strength after feedback on failure and success. Students' experiential responses conveyed mostly positive valence after feedback on success and mostly negative valence after feedback on failure. Behavioral observations showed more negative than positive emotion types after feedback on failure and more positive than negative emotion types after feedback on success. These results show that physiological arousal is a valuable objective indicator of emotional responses after immediate feedback but should be accompanied by other data streams in order to understand students' emotional responses. Both valence and emotion types can be used for this purpose. These outcomes pave the way for designing adaptive learning technologies that take students' emotions into account.

Inferring community transmission of SARS-CoV-2 in the United Kingdom using the ONS COVID-19 Infection Survey

Key epidemiological parameters, including the effective reproduction number, R(t), and the instantaneous growth rate, r(t), generated from an ensemble of models, have been informing public health policy throughout the COVID-19 pandemic in the four nations of the United Kingdom of Great Britain and Northern Ireland (UK). However, estimation of these quantities became challenging with the scaling down of surveillance systems as part of the transition from the “emergency” to “endemic” phase of the pandemic.The Office for National Statistics (ONS) COVID-19 Infection Survey (CIS) provided an opportunity to continue estimating these parameters in the absence of other data streams. We used a penalised spline model fitted to the publicly-available ONS CIS test positivity estimates to produce a smoothed estimate of the prevalence of SARS-CoV-2 positivity over time. The resulting fitted curve was used to estimate the “ONS-based” R(t) and r(t) across the four nations of the UK. Estimates produced under this model are compared to government-published estimates with particular consideration given to the contribution that this single data stream can offer in the estimation of these parameters.Depending on the nation and parameter, we found that up to 77% of the variance in the government-published estimates can be explained by the ONS-based estimates, demonstrating the value of this singular data stream to track the epidemic in each of the four nations. We additionally find that the ONS-based estimates uncover epidemic trends earlier than the corresponding government-published estimates.Our work shows that the ONS CIS can be used to generate key COVID-19 epidemiological parameters across the four UK nations, further underlining the enormous value of such population-level studies of infection. This is not intended as an alternative to ensemble modelling, rather it is intended as a potential solution to the aforementioned challenge faced by public health officials in the UK in early 2022.

Description of one species of freshwater amphipod Hyalella (Crustacea, Peracarida, Hyalellidae) from the northwest region of the state of Rio Grande do Sul, Southern Brazil.

In this study, a new species of Hyalella is described from southern region of Brazil. Hyalella jaboticabensis n. sp. was recorded in the municipality of Jaboticaba, northwest region of state of the Rio Grande do Sul, southern Brazil. This species is characterized by showing smooth body surface, epimeral plates not acuminated, inner face of the gnathopod 1 with nine or ten serrate setae and some simple setae (six to eight), inner ramus of uropod 1 of males with one short curved seta, peduncle of the uropod 3 with six cuspidate setae with an accessory seta and two or three cuspidate setae on the rami, telson as long as wide (square) with six to eight short and long cuspidate setae with an accessory seta. This new description increases the Brazilian known diversity of species of Hyalella to 42 (25 to southern Brazilian region) and 108 species in the Americas. It is noting that it is crucial to know the complete diversity of Hyalella genus, and to consider conservation measures to protect species like H. jaboticabensis n. sp., endemic to two single small streams.

An Imputing Technique for Surface Water Extent Timeseries with Streamflow Discharges

A continuous and multi-decadal surface water extent (SWE) record is vital for water resources management, flood risk assessment, and comprehensive climate change impact studies. The advancements in remote sensing technologies offer a valuable tool for monitoring surface water with high temporal and spatial resolution. However, challenges persist due to image gaps resulting from sensor issues and adverse weather conditions during data collection. To address this issue, one way to fill the gaps is by leveraging in situ measurements such as streamflow discharges (SFDs). We investigate the relationship between SFDs and Landsat-derived SWE in the New England region watersheds (eight-digit hydrological unit code (HUC)) on a monthly scale. While previous studies indicate the relationship exists, it remains elusive for larger domains. Recent research suggests using monthly average SFD data from a single stream gage to fill the gaps in SWE. However, as SWE represents a monthly maximum value, relying on a single gage with average values may not capture the complex dynamics of surface water. Our study introduces a novel approach by replacing the monthly average SFD with the maximum day streamflow discharge anomaly (SFDA) within a month. This adjustment aims to better reflect extreme scenarios, and we explore the relationship using ridge regression, incorporating data from all stream gages in the study domain. The SWE and SFDA are both transformed to stabilize the variance. We found that there is no discernible correlation between the magnitude of the correlation and the size of the basins. The correlations vary based on HUC and display a wide range, indicating the variances of the importance of stream gages to each HUC. The maximum correlation is found when the stream gage is located outside of the target HUC, further verifying the complex relationship between SWE and SFDA. Covering over 30 years of data across 45 HUCs, the imputing technique using ridge regression shows satisfactory performance for most of the HUCs analyzed. The results show that 41 out of 45 HUCs achieve a root-mean-square error (RMSE) of less than 10, and 44 out of 45 HUCs exhibit a normalized root-mean-square error (NRMSE) of less than 0.1. Of 45 HUCs, 42 have an R-squared (R2) score higher than 0.7. The Nash–Sutcliffe efficiency index (Ef) shows consistent results with R2, with the relative bias ranging from –0.02 to 0.03. The established relationship serves as an effective imputing technique, filling gaps in the time series of SWE. Moreover, our approach facilitates the identification and visualization of the most significant gages for each HUC, contributing to a more refined understanding of surface water dynamics.

Inertial co-focusing of heterogeneous particles in hybrid microfluidic channels with constantly variable cross-sections.

Heterogeneous particles co-focusing to a single stream is a vital prerequisite for cell counting and enumeration, playing an essential role in flow cytometry and single-cell analysis. Microfluidics-based inertial focusing holds great research prospects due to its simplicity of devices, ease of operation, high throughput, and freedom from external fields. Combining microfluidic channels with two or more different geometries has become a powerful tool for high-efficiency particle focusing. Here, we explored hybrid microfluidic channels for heterogeneous particle co-focusing. Four different annular channels with obstacles distributed on the inner wall were constructed and simulated, obtaining constantly variable secondary flows. Then we used four different fluorescent particles with the size of 10 μm, 12 μm 15 μm, and 20 μm as well as their mixture to perform the inertial focusing experiments of multi-sized particles. Theoretical simulation and experimental results demonstrated a focusing efficiency of >99%. Finally, we further utilized human white blood cells to estimate the co-focusing performance of our hybrid microfluidic channel, resulting in a high focusing efficiency of >92% and a high throughput of ≈8000 cell s-1. The hybrid microfluidic channels, capable of high-precision heterogeneous particle co-focusing, could pave a broad avenue for microfluidic flow cytometry and single-cell analysis.

Modeling instream temperature from solar insolation under varying timber harvesting intensities using RPAS laser scanning

Stream temperatures are influenced by the amount of solar insolation they receive. Increasing stream temperatures associated with climate warming pose detrimental health risks to freshwater ecosystems. In British Columbia (BC), Canada, timber harvesting along forested streams is managed using riparian buffer zones of varying widths and designations. Within buffer zones, depending on distance from the stream, selective thinning may be permitted or harvest may be forbidden. In this study, we used airborne laser scanning (ALS) point cloud data acquired via a remotely piloted aircraft system (RPAS) to derive forest canopy characteristics that were then used to estimate daily incoming summer and fall solar insolation for five stream reaches in coastal conifer-dominated temperate forests in Vancouver Island, BC, Canada. We then examined empirical relationships between estimated insolation and actual instream temperature measurements. Based on these empirical relationships, the potential effects of timber harvest on instream temperatures were simulated by comparing scenarios of different riparian forest harvest intensities. Our results indicated that modeled solar insolation explained 43–90 % of the variation in observed stream reach temperatures, and furthermore, when a single cold-water stream reach was excluded explained an overall 81 % of variation. Simulated harvesting scenarios generally projected increases in maximum stream reach temperatures 1–2 °C in summer and early fall months. However, in a full clearcut scenario (i.e. where all trees were removed), maximum stream reach temperatures increased as much as 5.8 °C. Our results emphasize the importance of retaining riparian vegetation for the maintenance of habitable temperatures for freshwater-reliant fish with thermal restrictions. In addition, we demonstrate the feasibility of RPAS-based monitoring of stream reach shading and canopy cover, enabling detailed assessment of environmental stressors faced by fish populations under climate warming.

Multi-Stream Concept Drift Self-Adaptation Using Graph Neural Network

Concept drift is the phenomenon where the data distribution in a data stream changes over time. It is a ubiquitous problem in the real-world, for example, a traffic accident would cause a jam in a certain period, leading to a distribution change in traffic speed. Most research in the concept drift field focuses on single data stream, however, few of them consider multi-stream environments which are more in line with the application needs. To fill this gap, we propose a multi-stream prediction setting and a multi-stream concept drift self-adaptation framework using graph neural network, named SAGN. In SAGN, we reconsider the learning procedure of GNN-based predictors from an aspect of concept drift adaptation for multi-stream. By this design, the prediction task is converted into online streaming data tasks in sub-graphs. Each sub-graph corresponds to an adaptation target and will be updated over time. In this way, locally we can overcome drift in each sub-graph by a designed adaptation technique, and globally the correlation between different data streams is well-preserved as a graph structure. Therefore, whether drift occurs or not, in one or several streams, SAGNcan provide consistently accurate prediction results. We comprehensively tested SAGNon both synthetic and real-world, drift and non-drift data in the multi-step prediction task. The experiment results show that SAGNis able to achieve state-of-the-art performance in most cases.

Human centric attention with deep multiscale feature fusion framework for activity recognition in Internet of Medical Things

Recent advancements in the Internet of Medical Things (IoMT) have revolutionized the healthcare sector, making it an active research area in the academic and industrial sectors. Following these advances, an automatic Human Activity Recognition (HAR) is now integrated into the IoMT, facilitating remote patient monitoring systems for smart healthcare. However, implementing HAR via computer vision is intricate due to complex spatiotemporal patterns, single stream fusion, and clutter backgrounds. Mainstream approaches practice pre-trained CNN model, which extract non-salient features due to their generalized weight optimization and limited discriminative feature fusion. In addition, their sequential models have inadequate performance in complex scenarios due to the vanishing gradients encountered during backpropagation across multiple layers. In response to these challenges, we propose a multiscale feature fusion framework for both indoor and outdoor environments to enhance HAR in healthcare monitoring systems, which is mainly composed of two stages: First, the proposed Human Centric Attentional Fusion (HCAF) network is fused with the intermediate convolutional feature of lightweight MobileNetV3 backbone to enriches spatial learning capabilities for accurate HAR. Next, a Deep Multiscale Features Fusion (DMFF) network is proposed that enhanced the long-range temporal dependencies by redesigning the traditional bidirectional LSTM network into a residual fashion followed by Sequential Multihead Attention (SMA) to eliminate non-relevant information and optimized spatiotemporal feature vectors. The performance of the proposed fusion model is evaluated on benchmark healthcare and general activity datasets. In the healthcare, we used Multiple Camera Fall and UR Fall Detection datasets that achieved 99.941% and 100% accuracy. Despite this, our fusion strategy is rigorously evaluated over three challenging general HAR datasets, including HMDB51,UCF101, and UCF50, demonstrating 74.942%, 97.337%, and 96.156% superior performance compared to State-of-The-Art (SOTA) methods. The run time analysis shows that the proposed method is 2x times faster than the existing methods.

High-throughput sorting of nanoparticles with light-patterned dielectrophoresis force

We present a size-based sorting method for nanoparticles in microfluidics with the aid of light-patterned dielectrophoresis (DEP) force. In a microfluidic channel, we have succeeded in manipulating a random distribution of particles into a single stream with the DEP force as well as the hydrodynamic force, and more strikingly, the trajectory of particles is found to be size-dependent, implicating that we can precisely separate nanoparticles based on their sizes even if they are identical in mass. We have numerically predicted the behavior of sorting nanoparticles, emphasizing on the size, velocity and electrical permittivity, so as to know their influences on the effective sorting, particularly in terms of high throughput. Our work confirms that what we believe to be the novel manipulation of nanoparticles features its flexibility as well as high throughput.

Distributed interferometric fiber tip biosensors for a multi-channel and label-free biomolecular interaction analysis.

This paper describes fabrication and implementation of distributed optical fiber tip biosensor probes for simultaneously measuring label-free biomolecular interactions at multiple locations. Biosensor probes at the tip of a single-mode fiber are Fabry-Perot etalons that are functionalized with a capture layer for a specific biomolecule. A coherence multiplexing method is implemented to separate data collected from distributed biosensors in a single data stream. Multiplexing is achieved by using fiber tip biosensors of varying etalon lengths with the same or different capture layers for each biosensing channel. Experiments demonstrating simultaneous multi-channel recording of protein-to-protein interaction sensorgrams with fiber tip biosensor probes are presented.