Patent Application
20230157587
Wearable electronics have been developed that can be worn by a user to continuously and closely monitor an individual's activities, such as walking or running. Such wearable electronics may include physiological sensors configured to sense certain physiological parameters of the wearer, such as heart rate, as well as motion sensors, GPS, radios, and altimeters.
Many of these electronic devices can be worn on or mated with human skin to continuously and closely monitor an individual's activities without unduly interrupting or limiting those activities. Biosensors on these wearable electronics may play a significant role in realizing personable medicine due to the capability for real-time monitoring of an individual's physiological biomarkers. Nonetheless, commercially available conventional wearable sensors are only currently capable of tracking an individual's physical activities and vital signs (e.g., step count, heart rate, etc.). They fail to provide insight into the user's health state at molecular levels.
To gain such insight into health state at a molecular level, human sweat is an excellent candidate for detection and measurement because it contains physiologically and metabolically rich information that can be retrieved non-invasively. Sweat analysis is currently used for applications such as disease diagnosis, drug abuse detection, and athletic performance optimization. Unfortunately, the sample collection and analysis are conventionally performed separately, thereby failing to provide a real-time profile of sweat content secretion, while requiring extensive lab analysis using bulky, and often expensive, instruments.
Development of wearable sweat biosensors has recently been explored where a variety of biosensors were used to measure analytes of interest. For example, U.S. Patent Application Publication No. US 2018/0263539 discloses a wearable sensing platform that includes sensors and circuits to sense aspects of a user's state by analyzing bodily fluids, such as sweat and/or urine, and a user's temperature. As described therein, a sensor array senses a plurality of different body fluid analytes, optionally at the same time. A signal conditioner is coupled to the sensor array. An interface is configured to transmit information corresponding to the conditioned sensor signals to a remote computing device. The wearable sensing platform may include a flexible printed circuit board to enable the wearable sensing platform, or a portion thereof, to conform to a portion of the user's body.
Recent emergence of wearable sweat sensors provides a promising future for non-invasive assessment of health physiology. To date, sweat sensors utilize conventional sweat induction approaches such as exercise, chemical, and thermal stimulation to obtain quantifiable sweat samples for on-body analysis (see, e.g., Yang et al. (2019) Nat. Biotechnol. DOI:10.1038/s41587-019-0321-x; Parlak et al. (2018) Sci. Adv. 4(7), eaar2904; Lee et al. (2017) Sci Adv. 3(3): e1601314; Yokus et al. (2020) Biosens. Bioelectron. 153: 112038; Emaminejad et al. (2017) Proc. Natl. Acad. Sci. USA, 114: 4625-4630; Jia et al. (2013) Anal. Chem. 85(14): 6553-6560; Kim et al. (2016) ACS Sens. 1(8): 1011-1019; Nyein et al. (2019) Sci. Adv. 5(8): eaaw9906; Alizadeh et al. (2018) Lab Chip, 18: 2632-2641; Bandodkar et al. (2019) Annu. Rev. Anal. Chem. 12: 1-22; Li et al. (2019) Small, 1903822; Koh et al. (2106) Sci. Trans. Med. 8(366): 366ra165; Twine et al. (2108) Lab Chip 18: 2816-2825; and the like). While these methods can provide large quantity of sweat in a short time (˜>2 μL cm−2 in 15 mins), (Hussain et al. (2017) Clin. Biochem. Rev. 38(1): 13-34) they require artificial sweat induction to enable sweat analysis. These types of sweat may not be suitable in all applications. Naturally secreting sweat is an under-utilized source that excretes voluntarily even when individuals are at rest (Hu et al. (2018) Br. J. Dermatol. 178(6): 1246-1256) and offers many promising applications and clinical interests. Natural sweat rate in infants is closely related to defects of the central nervous system and emotional sweating (Foster et al. (1971) Arch. Dis. Child. 46: 444-451; Harpin & Rutter (1982) Arch. Dis. Child 57: 691-695). It is associated with the cerebral cortex activity and is correlated with severity of paresis in patients with brain infarction (Satoh et al. (1965) Jpn. J. Physiol. 15: 523-531; Korpelainen (1993) Neurology, 43: 1211-1214). It is also linked to physiological habituation of soldiers to combat experiences (Wood et al. (2009) Mil. Med. 174: 1215-1222). Patients with underlying medical conditions such as autonomic dysfunctions such as diabetes, cerebrovascular diseases, and Parkinson's disease are also accompanied by abnormalities in sweat rate (Cheshire et al. (2003) Sem. Neurol. 23(4): 399-406). Additionally, natural sweat secretes at a slow rate, allowing enough time for biochemicals to permeate and partition between blood and sweat, and to achieve equilibrium conditions between these fluid compartments (Sonner et al. (2015) Biomicrofluidics, 9(3): 031301). Therefore, natural sweat compositions may provide a close relation with blood biomarkers.
Despite its promising applications and clinical interests, an inherent inaccessibility of natural sweat has hindered our capability to utilize its rich information for diverse physiological monitoring. Natural sweat generally secretes at a significantly lower rate (˜10 nL min−1 cm−2) than actively induced sweat (>250 nL min−1 cm−2) and evaporates quickly (Hussain et al. (2017) Clin. Biochem. Rev. 38(1): 13-34; Taylor et al. (2913) Extrem. Physiol. Med. 2: 4). To address this limitation, natural sweat analyses were previously conducted through sampling sweat on interfaces like wet absorbent pad and hydrogel. These methods utilized diffusion of sweat chemicals from the skin into the interface and allowed analytes accumulation over a period of time for detectable signals (Kintz et al. (2000) J. Anal. Toxicol. 24: 557-561; Leggett et al. (2007) Angew. Chem. Int. Ed. 46: 4100-4103; Lin et al. (2019) ACS Sens. DOI: 10.1021/acssensors.9b01727). However, they do not allow monitoring temporal changes in sweat compositions. Low, stimulated sweat composition analyses were previously demonstrated using nafion and thiol derivatives as wicking media (Twine et al. (2108) Lab Chip 18: 2816-2825; Hauke et al. (2018) Lab Chip 18: 3750-3759; Lee et al. (2016) Nat. Nanotechol. 11: 566-572). They could neither collect sweat nor provide sweat rate. Continuous natural sweat rate analyses have traditionally been done in the hospital by monitoring humidity changes on the skin in a capsule such as in autonomic testing (Illigens & Gibbons (2009) Clin. Auton. Res. 19 (2), 79-87). Nevertheless, the use of bulky instrumentations for these sweat analyses has restricted the applications to clinical settings. The challenge remains in devising a wearable device that allows effective natural sweat capture and analyzes continuous sweat profile for routine assessment.
In various embodiments a microfluidic patch is provided that allows continuous analysis of natural sweat at various body locations of sedentary or active individuals. By modelling sweat glands and microfluidics according to the Poiseuille's law, in certain embodiments devices are provided comprising microchannels interfaced with a hydrophilic filler that can detect sweat rate down to 2 nL min−1 cm−2 even at the lowest secretion regions like wrist within an hour of device application. In certain embodiments the device is integrated with an electrical sweat rate sensor and electrochemical sensors to enable simultaneous detection of sweat rate and compositions such as pH, Cl—, levodopa, and the like. In certain embodiments the devices provide for dynamic sweat analysis related to light physical activities, hypoglycemia-induced sweating, and levodopa sensing for Parkinson's disease management. The device enables routine analysis of natural sweat dynamics arising from different physical and physiological functions that cannot be realized by current wearable sweat sensors. This can facilitate new sweat investigations related to individuals' well-being such as infant care, stroke rehabilitation, psychiatric assessment, and soldier welfare.
Accordingly, various embodiments provided herein may include, but need not be limited to, one or more of the following:
Embodiment 1: A wearable biometric monitoring system comprising:”
Embodiment 2: The wearable biometric monitoring system of embodiment 1, wherein device comprises a collection well 108 in fluid communication with said microfluidic channel and said hydrophilic material 106 is disposed in said collection well.
Embodiment 3: The wearable biometric monitoring system according to any one of embodiments 1-2, wherein said collection well provides a collection area ranging in diameter from about 1 mm to about 20 mm, or from about 2 mm up to about 10 mm, or from about 3 mm up to about 7 mm.
Embodiment 4: The wearable biometric monitoring system according of embodiment 3, wherein said collection well provides a collection area of about 8 mm.
Embodiment 5: The wearable biometric monitoring system according of embodiment 3, wherein said collection well provides a collection area of about 5 mm.
Embodiment 6: The wearable biometric monitoring system according of embodiment 3, wherein said collection well provides a collection area of about 3 mm.
Embodiment 7: The wearable biometric monitoring system according to any one of embodiments 1-6, wherein said hydrophilic material is laminated and includes hydrogel 204.
Embodiment 8: The wearable biometric monitoring system according to any one of embodiments 1-7, wherein said hydrogel comprises an agarose-glycerol (AG-GLY) hydrogel.
Embodiment 9: The wearable biometric monitoring system according to any one of embodiments 1-8, wherein said hydrophilic material comprises a hydrophilic polymer disposed on a patterned substrate.
Embodiment 10: The wearable biometric monitoring system of embodiment 9, wherein said hydrophilic polymer comprises polyvinyl alcohol (PVA).
Embodiment 11: The wearable biometric monitoring system according to any one of embodiments 1-10, wherein said patterned substrate comprises a patterned epoxy substrate.
Embodiment 12: The wearable biometric monitoring system of embodiment 11, wherein said substrate comprises a patterned SU8 substrate.
Embodiment 13: The wearable biometric monitoring system according to any one of embodiments 1-12, wherein said hydrophilic material comprises laminated substrate comprising a hydrophilic polymer disposed on a patterned substrate that is coated with a hydrophilic polymer.
Embodiment 14: The wearable biometric monitoring system according to any one of embodiments 1-13, wherein said microfluidic channel has a length of less than 33 cm, or less than 30 cm, or less than 25 cm, or less than 20 cm, or about 15 cm or less.
Embodiment 15: The wearable biometric monitoring system according to any one of embodiments 1-14, wherein said microfluidic channel has a minimum volume of about 750 nL.
Embodiment 16: The wearable biometric monitoring system according to any one of embodiments 14-15, wherein said microfluidic channel has a length of about 15 cm or less.
Embodiment 17: The wearable biometric monitoring system according to any one of embodiments 1-16, wherein said microfluidic channel has dimensions that provide a flow rate drop of less than about 10% along the length of said microfluidic channel.
Embodiment 18: The wearable biometric monitoring system according to any one of embodiments 1-17, wherein said microfluid channel has a cross-section area at least about 2,209 μm2 (e.g., 47 μm×47 μm), or at least about 3600 μm2, or at least about 4900 μm2 (e.g., 70 μm×70 μm), or at least about 700 μm2, or at least about 14,000 μm2 (e.g., 200 μm×70 μm).
Embodiment 19: The wearable biometric monitoring system of embodiment 18, wherein said microfluidic channel has a cross-section area of about 70 μm×70 μm.
Embodiment 20: The wearable biometric monitoring system of embodiment 18, wherein said microfluid channel has a cross-section area of about 200 μm×70 μm.
Embodiment 21: The wearable biometric monitoring system according to any one of embodiments 1-20, wherein said sensing electrode(s) 104 are configured to be in fluid communication with a fluid in said microfluidic channel.
Embodiment 22: The wearable biometric monitoring system of embodiment 21, wherein said sensing electrodes 104 are configured to be aligned with the microfluidic channel 110.
Embodiment 23: The wearable biometric monitoring system according to any one of embodiments 21-22, wherein said sensing electrodes 104 are configured as two interdigitated wheel-shaped electrodes aligned with the microfluidic channel 110.
Embodiment 24: The wearable biometric monitoring system according to any one of embodiments 21-23, where said sensing electrodes comprise sweat rate sensing electrode(s) 104a and analyte detecting electrodes 104b.
Embodiment 25: The wearable biometric monitoring system of embodiment 24, wherein said sweat rate sensing electrodes 104a comprise radial conductive electrodes 104a1.
Embodiment 26: The wearable biometric monitoring system according to any one of embodiments 24-25, wherein said analyte detecting electrodes 104b comprise one or more regions 104b1 functionalized for detection of pH and/or an analyte.
Embodiment 27: The wearable biometric monitoring system of embodiment 26, wherein said analyte detecting electrode(s) 104b are functionalized for detection and/or quantification of an analyte selected from the group consisting of a metabolite, a drug, ethanol, a metal ion, and a salt.
Embodiment 28: The wearable biometric monitoring system according to any one of embodiments 1-27, wherein sensing electrode 104 is configured to measure sweat rate.
Embodiment 29: The wearable biometric monitoring system according to any one of embodiments 1-28, wherein sensing electrode 104 is configured to measure pH, Cl−, and/or levodopa.
Embodiment 30: The wearable biometric monitoring system of embodiment 29, wherein said system measures pH.
Embodiment 31: The wearable biometric monitoring system of embodiment 29, wherein said system measures Cl−.
Embodiment 32: The wearable biometric monitoring system of embodiment 29, wherein said system measures levodopa.
Embodiment 33: The wearable biometric monitoring system according to any one of embodiments 1-32, wherein said system is configured for detection by detection and/or quantification of electrical current or electrical potential.
Embodiment 34: The wearable biometric monitoring system according to any one of embodiments 1-33, wherein said microfluidic channel 110 is disposed in a microfluidic chip 102.
Embodiment 35: The wearable biometric monitoring system according to any one of embodiments 1-20, wherein said device is disposed on a flexible substrate 112.
Embodiment 36: The wearable biometric monitoring system of embodiment 35, wherein said substrate a flexible polymer.
Embodiment 37: The wearable biometric monitoring system of embodiment 36, wherein said substrate comprises polyethylene terephthalate (PET).
Embodiment 38: The wearable biometric monitoring system according to any one of embodiments 1-37, wherein said wearable biometric monitoring system comprises a skin adhesive 114 compatible with application to the skin.
Embodiment 39: The wearable biometric monitoring system of embodiment 38, wherein said skin adhesive 114 is disposed so that when said device is attached to the skin of a subject, said collection well is juxtaposed against a surface of said skin.
Embodiment 40: A wearable patch for analysis of a user's sweat comprising:
wherein said skin adhesive is capable of attaching said microfluidic chip to the skin of a user and said hydrophilic material is capable of drawing sweat from said user so that said sweat can be transported into said microfluidic channel and to said electrode for analysis.
Embodiment 41: The wearable patch of embodiment 6 wherein said sensing electrode measures sweat rate.
Embodiment 42: The wearable patch of embodiment 6 wherein said sensing electrode is an electrochemical sensor which senses pH, Cl− and/or levodopa.
Embodiment 43: A method of analyzing a user's sweat comprising:
Embodiment 44: A method of analyzing a subject's sweat, said method comprising:
Embodiment 45: The method of embodiment 44, wherein said monitoring system is operated to detect the sweat rate of said subject.
Embodiment 46: The method according to any one of embodiments 44-45, wherein said monitoring system is operated to determine the pH of the sweat of said subject.
Embodiment 47: The method according to any one of embodiments 44-46, wherein said monitoring system is operated to detect an analyte in the sweat of said subject where said analyte is selected from the group consisting of a metabolite, a drug, ethanol, a metal ion, and a salt.
Embodiment 48: The method of embodiment 47, wherein said monitoring system is operated to detect and/or quantify pH, Cl−, and/or levodopa in the sweat of said subject.
Embodiment 49: The method of embodiment 48, wherein said monitoring system is operated to measure Cl− in the sweat of said subject.
Embodiment 50: The method of embodiment 48, wherein said monitoring system is operated to measure levodopa in the sweat of said subject.
Embodiment 51: The method according to any one of embodiments 44-50, wherein said subject is a human.
Embodiment 52: The method according to any one of embodiments 44-50, wherein said subject is a non-human mammal.
The difficulty of accessing naturally secreting sweat has limited the ability to explore and utilize its rich information for non-invasive health assessment in sedentary individuals without active sweat induction. To address this, we developed a wearable patch that provides natural sweat collection and continuous analysis at various body parts. By devising a small microfluidic device with a hydrophilic filler (e.g., a laminated hydrophilic filler) and sweat sensors, we enable continuous sweat collection and analysis at low secretion sites like wrist even when the subject is physically inactive. It is demonstrated herein that the patch can track sweat variations arising from light physical activities, metabolic changes due to insulin injection and drug administration to assist Parkinson's disease management.
Accordingly, in certain embodiments a wearable microfluidic device to measure natural sweat secretion rates and compositions is provide as well as uses thereof. The device presents an important advancement in wearable sweat sensing by allowing continuous perspiration analysis without artificial sweat induction in sedentary individuals. It enables local sweat rate measurements even at the lowest sweat secretion regions and allows investigation of relation between perspiration and physical and physiological functions. We overcome the challenge of accessing natural sweat through the use of a microfluidic device embedded with a hydrophilic filler (e.g., a laminated hydrophilic filler) inside a collection well. The filler minimizes the dead volume originated from the well and enhances sweat transport with minimal lag time.
In various embodiments the device dimensions were designed through consideration of the flow resistance in sweat glands and microfluidic channels according to the Poiseuille's law. By integration of an electrical sensor for sweat rate monitoring and electrochemical sensors for pH, Cl−, levodopa (or other analyte) detection, we enabled continuous analysis of natural sweat rate and composition. As described in Example 1, we utilized the device to measure natural sweat secretion rates on various locations of a human subject including shoulder, chest, bicep, wrist, abdomen, thigh, and leg, and finger. We also explored dynamic sweat behaviors during light physical activities, hypoglycemia, and control drug administration for Parkinson's disease management.
The device(s) described herein proves to be an ideal platform to continuously or routinely monitor users' medical conditions and physiological status during daily routines. They can also advance sweat investigations beyond what current wearable sweat sensors can provide by promoting a fundamental understanding of natural sweat secretion and its relation to diverse health conditions.
Components of one embodiment of a microfluidic sweat analysis patch designed to enable effective small volume collection and analysis of natural sweat are schematically illustrated in
In certain embodiments the microfluidic layer 102 (e.g., microfluidic chip) is aligned and bonded together with the sweat sensing electrodes 104 so that the sweat sensing electrodes are in contact with a fluid (e.g., sweat) in the microfluidic channel(s) 110. In various embodiments the sensing electrodes are configured for detection of sweat rate (e.g., as an impedance-based sweat rate detector. In certain embodiments the sensing electrodes are functionalized for detection of physiological analytes, e.g., pH, Cl—, levodopa and other drugs, and the like.
In the embodiment illustrated in
Finally, in various embodiments, the collection well 108 is filled with a patterned hydrophilic filler comprising for example an SU8 filler coated with a thin saturated hydrogel layer (see, e.g.,
Microfluidic Channel(s)
It will be noted that in various embodiments, the microfluidic channel 110 can be provided in any of a number of configurations and configured with a size and shape to optimize channel volume. In certain embodiments the microfluidic channel 110 comprises a serpentine/convoluted channel to increase channel length. In certain embodiments the microfluidic channel 110 comprises a circular spiral serpentine channel, an oval spiral serpentine channel, a square spiral serpentine channel, a switchback serpentine channel, a branched channel pattern, and the like. In certain embodiments the microfluidic channel can comprise a single channel or a plurality of microfluidic channels. As noted above, in the embodiment illustrated in
With respect to the microfluidic channel 110 configuration and dimensions, it is noted that the natural sweat secretion rates of humans varies significantly with body location, For example, sweat secretion rates can be lower than 10 nL min−1 cm−2 at low secretion sites such as the arm or leg, and can reach on the order of 100 nL min−1 cm−2 at high secretion areas like the palm and foot. Such secretion rates, however, are small compared to typical sweat rates obtained by active sweat stimulation, which can be higher by an order of magnitude.
To enable low natural sweat rate measurement inside the microchannel, the channel cross-section needs to be as small as possible such that temporal variations in secretion rate can be resolved. However, the microchannel 110 length needs to be long enough to enable long-term measurement on desired body location. Accordingly, in certain embodiments, the microfluidic device is configured to contain ˜750 nL or greater such that sweat analysis can be done longer than an hour at the lowest sweat rate sites. Toward this goal, two constraints were considered:
To meet these criteria, we examined the fluid resistance of the sweat glands and the microchannel based on the Poiseuille's law. In particular, as described in Example 1, the smallest dimensions enabled to reach the same fluid resistance as that of the sweat glands were computed.
Based on the calculations, a microfluidic channel cross-sectional area of 47 μm×47 μm satisfied the first constraint, and a channel length of 33 cm achieved a minimum volume of about 750 nL. However, this alone does not tell the flow rate variation along the channel length. Therefore, as described in Example 1, we next examined the flow rate variation due to the viscous resistance along the microchannel.
It was determined that flow rate decreases as sweat travels deeper into the channel, and the effect becomes more apparent as the channel width decreases and the length increases. As illustrated in Example 1, to contain a volume of ˜750 nL and a flow rate drop of less than 10%, it is desirable to have the cross-sectional area above 70 μm×70 μm and the microfluidic channel 110 length shorter than about 15 cm. These dimensions also satisfy the first constraint. Therefore, we chose two cross-sectional areas, 70 μm×70 μm and 200 μm×70 μm with lengths shorter than 15 cm to monitor sweat rates in low and high secretion regions respectively.
Hydrophilic Material.
It is ideally beneficial for microfluidic collection area (e.g., the area of the collection well 108 juxtaposed to the skin 118) to be large to maximize the accessible sweat glands. However, a large collection area creates a dead volume that must first be filled with sweat before the sweat flows into the microchannel. This creates a lag time in the sensor's response. To address this problem, we incorporated a hydrophilic filler to occupy the dead volume and to draw sweat readily into the channel as soon as it secretes. Accordingly, in certain embodiments the hydrophilic filler 106 comprises a hydrogel (e.g., an agarose-glycerol (AG-GLY) hydrogel 204) (see, e.g.,
The structure and function of the hydrophilic insert 106 in the collection chamber 108 is also schematically illustrated in
The time required for initiating sweat analysis using the device described in Example 1 is slightly longer than the theoretical calculations demonstrated for stimulated sweat in previously proposed device. However, with modification of the hydrophilic material and device design, it is possible to enhance the time required to initiate natural sweat analysis. Unlike prior devices that utilize hydrophilic material that has direct contact with the sensor and the skin for compositional analysis of stimulated sweat, the device described herein separates the hydrophilic filler from the sensing channel such that sensor will not be affected by the film and controls exact amount of fluid in the sensing channel for consistent sensor readings. Using the device, described herein detection of flow rate as low as 2 nL min−1 was enabled.
Sensor Electrodes.
In order to utilize microfluidic, deice described herein for electrical measurement, electrical sensing electrodes 104 are incorporated into the device. In the illustrative, but non-limiting embodiment shown in
The sweat rate sensor was first characterized by measuring admittance in different concentrations of NaCl solutions at an operating frequency of 100 kHz as described in Example 1. This frequency was chosen to minimize the capacitance contribution of the impedance and to maximize the resistive part of the impedance measurement. The relationship between admittance and fluid volume in the channel for NaCl concentrations of 10, 50, 100, and 200 mM was determined and it was demonstrated that, that at higher NaCl concentrations, the admittance between the electrodes increases due to the higher conductivity of increasing ion concentrations. Additionally, increasing fluid volume in the channel gives rise to higher admittance as more ionic solution is in contact with a larger area of the electrodes, decreasing the resistance between the electrodes. To demonstrate the reliability and reproducibility of the sweat rate sensors, it was also necessary to show that the time interval between admittance pulses are the same for a given flow rate in the channel and for fluid volume between the two contacts regardless of ions concentration. Using a commercial syringe pump, 10 and 200 mM NaCl solutions were flowed at a constant rate of 250 nL min−1 into the sweat rate sensor and the volumetric increments between consecutive contacts is determined as a function of time. In comparing the 10 mM and 200 mM volumes it was observed that the pulses occur at the same time, indicating a reproducible calculation of sweat rate.
Lastly, to verify that our sweat rate device accurately returns the correct flow rate, the measured flow rate calculated from our sweat rate sensor was compared against the known input pump rate of a commercial syringe pump system. The syringe pump was used to flow 200 mM NaCl inside the microfluidic channel at an input rate of 150 nL min−1 and 400 nL min−1 as described in Example 1 and it was observed that the input pump rate is in agreement with the measured flow rate from the device.
Additionally, electrochemical sensors that have a sensing area of 200 μm by 200 μm each were characterized. In the configuration shown in
The wearable devices described herein find utility in a wide variety of applications. In various embodiments they can readily be used to detect sweat rate and/or one or a plurality of analytes.
In certain embodiments the devices are used to provide natural perspiration analysis during light physical activities. By way of illustration, as described in Example 1, the microfluidic patch was first used to monitor sweat dynamics to demonstrate if sweat can track different physical activities of a sedentary subject while performing routine tasks. The patch was placed on the wrist of a healthy volunteer, along with a heart rate monitor. Heart rate and sweat rate were simultaneously monitored for 6 hours. Results in showed that wrist sweat rate closely tracks heart rate arisen from various physical activities such as taking a walk and performing lab work.
We additionally conducted on-body sweat analysis on the finger and the wrist of a volunteer subject. A collection well of 3 mm diameter was used on the finger while an 8 mm diameter was used on the wrist for sweat analyses. This allowed hour-long measurement on both finger and wrist based on measured flow rates. Similar to the previous study, sweat rate, in general, closely tracked heart rate variations. Sweat pH remained stable at 6.8 and 7.1 on the finger and wrist throughout the measurement period. Sweat Cl showed slight variation initially and stabilized around 22 and 40 mM on finger and wrist respectively.
Resolution of wrist sweat rate can be enhanced by increasing number of radial electrodes in sweat rate sensors as discussed previously. Under our experimental conditions, we consistently observed perspiration in short time intervals (in second for the finger and in minutes for the wrist) throughout the day. Due to its ability to closely track different activities, the devices described herein can be beneficial for sweat investigations associating with physical and mental stress-induced sweat.
In certain embodiments the devices described herein can be used for the detection and/or quantification of sweat secretion induced by metabolic changes. By way of non-limiting illustration, as described in Example 1, the patch was utilized to investigate hypoglycemia-induced sweat secretion. In diabetic patients, injection of insulin gives rise to hyperhidrosis due to hypoglycemia. They can also be vulnerable to irregular heartbeat, which can be life-threatening. Understanding sweating and heart complications in diabetic patients, hence, can facilitate diabetes management. Toward this aim, we performed simultaneous monitoring of heart rate, sweat rate, and interstitial fluid (ISF) glucose levels to explore heart and sweat complications during large glucose variation. A diabetic subject wore the microfluidic patch on the finger along with a pulse oximeter. The measurement was done without interrupting the routine insulin injection procedures of the diabetic patient. During the measurement duration, the subject was asked to remain sitting without vigorous movements. Blood glucose was measured right before the measurement began and after it ended. ISF glucose data was recorded via Dexcom G6 continuous glucose monitor. As described in the trials in Example 1, glucose was initially high when the measurement began, and the sweat rate remained relatively low between 0.5 and 1 μL min−1 cm−2. After insulin was injected, glucose started to decrease rapidly. In the meantime, an increase in sweat rate was observed. When glucose further decreased lower than 90 mg/dL there was a dramatic increase in sweat rate up to 5 μL min−1 cm−2. Heart rate remained relatively unchanged during low glucose level. Based on our results, significant decrease in glucose level is accompanied by a rise in sweat rate while no clear heart rate irregularity is observed and this is readily detecting using the devices described herein.
In various embodiments the devices described herein can be used to detect and/or quantify one or a plurality of analytes. Illustrative analytes include, but are not limited a metabolite, a drug, ethanol, a metal ion, and/or a salt.
By way of non-limiting illustration, as described in Example 1, the devices described herein were used for levodopa sensing, e.g., for Parkinson's disease management. Levodopa is a first-line drug for treating Parkinson's disease. It has been reported that long-term intermittent oral dosage of L-dopa causes fluctuation in plasma levodopa concentrations and leads to unpredictable responses such as motor fluctuations and dyskinesia; thus, continuous monitoring of L-dopa is important to circumvent such unforeseen responses.
Sweat has been reported to contain foreign drugs, including levodopa. Sweat is a promising non-invasive way to continuously monitor levodopa level inside the body. It may also facilitate finding an optimal dosage and interval that is personalized to each patient. Additionally, Parkinson's patients usually suffer from abnormal sweating. Hyperhidrosis occurs when the blood levodopa concentration is low, therefore, studying sweat behavior and monitoring levodopa concentration can assist management of Parkinson's disease.
As described in Example 1, we conducted on-body trials to study how sweat levodopa evolves within the body. A healthy subject was asked to consume 100 and 200 g intake of broad beans which contain levodopa to observe sweat levodopa relation to broad beans intake. In this study, boiled broad beans which were reported to contain approximately 0.6% levodopa were used. Levodopa sensors were calibrated in sweat as shown in Example 1 to ensure measurement accuracy. A sweat collection well of 3 mm diameter was used. It was observed that levodopa was detected in sweat approximately 20 mins after initial intake and its concentration peaked at 35 mins after intake. The peak concentration was measured to be approximately 13 μM when the subject had 1 dose of levodopa (1 dose of levodopa=100 g of broad beans).
In another experiment, the subject again consumed 200 g of broad beans, and levodopa was measured approximately 20 mins after initial intake. Its concentration peaked at 35 μM, 30 minutes after initial intake and slowly decreased. Additional trials showed similar results. We observed that levodopa concentration in sweat increases with increasing doses. When other foods with minimal levodopa is consumed, no significant signal is observed. This indicates that monitoring sweat levodopa is a promising way to keep track of blood levodopa to assist medication management of Parkinson's disease patients.
In conclusion, the devices described herein provide continuous analysis of naturally secreting sweat at diverse locations of sedentary individuals. Based on our studies, the device is ideal for monitoring natural sweat behavior while performing day-to-day indoor activities. Our study of sweat dynamics based on physical and physiological changes also shows its promising future sweat applications and clinical investigations related to passive perspiration. The devices described herein may actualize routine health and psychological assessment such as emotional contentment and development of infants, rehabilitation after stroke and recovery from combat stress through further sweat investigations. They may also help discover new sweat relations to physiological and medical conditions by gleaning insight into natural sweat profile of individuals
The following examples are offered to illustrate, but not to limit the claimed invention.
The body naturally and continuously secretes sweat for thermoregulation during sedentary and routine activities at rates that can reflect underlying health conditions, including nerve damage, autonomic and metabolic disorders, and chronic stress. However, low secretion rates and evaporation pose challenges for collecting resting thermoregulatory sweat for non-invasive analysis of body physiology. Here we present wearable patches for continuous sweat monitoring at rest, using microfluidics to combat evaporation and enable selective monitoring of secretion rate. We integrate hydrophilic fillers for rapid sweat uptake into the sensing channel, reducing required sweat accumulation time towards real-time measurement. Along with sweat rate sensors, we integrate electrochemical sensors for pH, Cl—, and levodopa monitoring. We demonstrate patch functionality for dynamic sweat analysis related to routine activities, stress events, hypoglycemia-induced sweating, and Parkinson's disease. By enabling sweat analysis compatible with sedentary, routine, and daily activities, these patches enable continuous, autonomous monitoring of body physiology at rest.
Device Structure.
Our microfluidic device shown in
Device Design.
Humans' sweat secretion rates at rest vary across different body locations on average. For instance, sweat secretion rates can be lower than 10 nL min−1 cm−2 at low secretion sites such as arm and leg, and can reach on the order of 100 nL min−1 cm−2 at high secretion areas like the palm and foot29,30. Such secretion rates are small compared to typical sweat rates obtained by active sweat stimulation, which can be higher by an order29,31. To enable low resting sweat rate measurement inside the microchannel, the channel cross-section needs to be as small as possible such that temporal variations in secretion rate can be resolved by allowing fast speeds of the moving sweat front. At the same time, the channel resistance cannot be so high as to limit flow in the channel and potentially suffocate sweat gland secretion, so the channel cross section cannot be too narrow. Finally, the channel length needs to be long enough for the device to have sufficient volumetric holding capacity to enable long-term measurement on desired body locations. Here, we aim to develop a microfluidic device that can contain ˜750 nL or greater such that sweat analysis can be done longer than an hour at the lowest sweat rate regions. Toward this goal, we estimated secretory pressures of the sweat gland spanning a broad range of resting sweat secretion rates from 3 to 1 μL min−1 cm−2. We established that the channel contributes to most of the device hydraulic resistance compared to the collection well. For various square cross-sectional areas and associated channel lengths that give close to 750 nL holding capacity, we calculated hydraulic pressure losses and compared these to the secretory pressure of the grand. From this, we established that a channel cross section of 70 μm×70 μm with ˜15 cm length has low enough resistance to support sweat flow across low to high secretory rates. Detailed calculations of this procedure are reported on in the Supplementary Information. Based on these results, we chose two cross-sectional areas of the spiraling microfluidic portion for sweat rate measurement, 70×70 μm (design 1) and 200 μm×70 μm (design 2) as depicted in
It is ideally beneficial for microfluidic collection area to be large to maximize the accessible sweat glands. However, a large collection area creates a dead volume, in which sweat firstly needs to be filled before flowing into the microchannel. This creates a lag time in sensor's response. To address this problem, we incorporated a hydrophilic filler, containing a patterned SU8 mold and hydrogels, to occupy the dead volume and to draw sweat readily into the channel as soon as it secretes. Hydrogels have been used extensively in the wearable electronics community to create soft interfaces and to absorb and hold biofluids onto sensor surfaces, but deploying gels to enhance sweat replacement times and minimize accumulation volumes and lag times represents a key advantage in this work32-34. This structure overall comprises of a PVA-coated rigid SU8 component that is first inserted into the well and overlayed with an agarose-glycerol hydrogel that directly contacts skin for sweat uptake (
By addition of the deformable AG-GLY gel35 with high hydrophilicity, sweat from the collection area can be drawn into the gel and transported to the microchannel more effectively. Therefore, the AG-GLY film covers the top surface of the filler and is directly in contact with the skin. Without the hydrophilic filler, volumetric calculations show that a collection well with a 5 mm diameter and a 400 μm thickness will require more than 2 h to fill the well if sweat secretes at 300 nL min−1 cm−2 while taking over 30 min and 200 h for extreme rates of 1 μL min−1 cm−2 and 3 nL min−1 cm−2, respectively. The integration of the hydrophilic filler enhances the collection and transports fluid into the channel within a few minutes. For a 5 mm diameter collection area, the film can hold a liquid volume of nearly 200 nL in the well. For 300 nL min−1 cm−2, it takes approximately 3 min to fill the well and initiate the sweat analysis. Similarly, it takes under a minute for a rate of 1 μL min−1 cm−2 near the upper range of resting sweat secretion or around 30 min for rates toward the 3 nL min−1 cm−2 lower end when appropriately sized collection wells are used. The experimental result using a syringe pump supports this conclusion as shown in the supplementary materials. For typical resting sweat rates ˜<30 nL min−1 cm−2 29, the difference in lag time is more apparent (˜30 min instead of ˜ a day), and sweat measurement is almost impractical for a hollow PDMS well. Note that due to its small footprint and the fact that the sensing patch is held tightly against skin via medical adhesives, the hydrogel cannot swell so much that it pushes off from the skin surface and delaminates the patch. Instead, as the hydrogel uptakes sweat, the tight seal against skin forces the hydrogel to expel this sweat into the channel. This supports rapid and leakproof collection of resting sweat in the channel. With further investigation of the hydrophilic film and device design, it is possible to enhance the time required to initiate thermoregulatory sweat analysis at rest. Unlike prior devices which utilize hydrophilic material that has direct contact with the sensor and the skin for compositional analysis of stimulated sweat22,25,36, our device separates the hydrophilic filler from the sensing channel such that the sensor surface is not impacted by fluid and pressure variations in the film, and to control and fix the amount of fluid in the sensing channel for consistent sensor signals. Using the device, we also enable detection of flow rate as low as 2 nL min−1 as presented in
Due to low resting sweating rates and the dimensions of the well and channel, we expect some diffusion and Taylor dispersion of analyte concentrations between when sweat is secreted on the skin surface and when it arrives at the electrochemical sensors near the entry of the channel. We perform a careful study of the time lags associated with this spread of analyte profiles in the Supplementary Information. Regions like the fingertips and hands are established to have relatively higher resting sweating rates, for which our simulations indicate a time lag of around 3 min30. This lag presents a limit on how updated the continuously made measurements are, but is well below the time scale over which physiological changes are expected to be manifested in sweat. At lower rates, sweat intrinsically moves more slowly through the device and takes longer to arrive at the sensors, allowing more time for dispersion effects. In contrast, because sweat rate is measured simply by the rate of fluid front movement, continuous and updated sweat rate measurements can be made with negligible time lag once sweat enters the channel.
Device Feasibility for Sweat Collection at Rest.
It is important to explore at-rest thermoregulatory sweat secretion rate as it is modulated not only by environmental conditions and physical activities but also by mental stimulation and underlying health conditions7,8,37-40. Tracking sweat secretion routinely may help discover valuable insights into human physiology (
The subject was asked to refrain from moderate to vigorous physical activities during the 24-h time frame. An example of the collection area and the imaging area of the patches are displayed in
6 ± 3.3
9 ± 1.2
According to our results, the finger has the highest secretion rate that can range between the order of 0.1 and 1 μL min−1 cm−2. All other regions show relatively low secretion rate of 1-20 nL min−1 cm−2. The results agree with the literatures which showed that palm and fingers have the highest secretion rate29. Majority of our measured sweat rates are slightly lower than reported rates in literatures possibly due to lower environmental temperatures and humidity used in the experiments. To demonstrate the reproducibility of the sweat rate measured by the patch, we also conducted a trial where we had a subject wearing the patches on two adjacent locations on the thigh. The data is displayed in
There are a few factors that may induce uncertainty in measured sweat rates values. They include possible sweat migration into the collection area from other parts underneath the patch. In addition, there is a possibility of higher sweat rate in the collection area to make up for the perspiration that may be hampered in the rest part of the device. These factors can result in overestimation of the measured sweat rates; however, the relative sweat rates will not differ. To investigate the first concern, we spot colored dye on the underside of the patch. After device removal, we observe that skin is dyed just in the region of the collection well and not in surrounding regions, confirming that there is no lateral sweat leakage or transfer from the collection well, and all sweat produced in that area is forced into the device for measurement. The dyed sweat can be visually monitored as it flows in the channel to optically validate electrical sweat rate measurements or as an independent visual measurement scheme enabled by this patch. This scheme for optical sweat rate tracking is realized via discrete photographs of sweat progression within the channel as in
As for the second factor that could impact sweat rate accuracy, namely compensatory sweating effects, all devices covering sweat glands can induce the same effect, and this requires careful studies in the future. Local heat generation due to on-body attachment of the patch must also be considered as it could potentially elevate sweating rates18, but negligible local heating is observed as demonstrated in
Sensors Characterization.
In order to utilize the microfluidic patch for electrical measurement, electrical sensing electrodes are incorporated into the microfluidic. As shown in
A larger number of radial electrodes allows for higher temporal resolution of sweat rate measurements. Electrochemical sensors located at the end of the semicircular electrodes are aligned with the microchannel as shown in
The sweat rate sensor (200 μm×70 μm) was first characterized by measuring admittance in different concentrations of NaCl solutions at an operating frequency of 100 kHz. This frequency was chosen to minimize the capacitance contribution of the impedance and to maximize the resistive part of the impedance measurement.
We further characterized the electrochemical sensors which have a sensing area of 200 μm by 200 μm each, given the 200 μm width of the functionalized electrode tips and the 200 μm width of the microfluidic channel in between the collection well and spiraling portion (as depicted in
To further investigate the flow effect on the sensors' performances upon integration into the microfluidic channel, we performed flow dependence test as shown in
Near-Rest Perspiration Analysis During Light Physical Activities.
The microfluidic patch was first used to monitor sweat dynamics to demonstrate if sweat can track different physical activities of a sedentary subject while performing routine tasks (
Sweat Analysis to Detect Stress Events Over 24 h.
The patch was next worn on the fingertip of a healthy volunteer during two trials, 24 h each, with routine activity including eating, walking, and sleeping, while heart rate and ambient temperature were monitored simultaneously. The subject was mostly sedentary and performed intervals of public speaking including giving a presentation and answering questions in a live streamed conference in Trial 1 (
Sweat Secretion Induced by Metabolic Changes.
The patch was further utilized to investigate hypoglycemia-induced sweat secretion. In diabetic patients, injection of insulin gives rise to hyperhidrosis due to hypoglycemia43,44. They can also be vulnerable to irregular heartbeat, which can be life-threatening45. Understanding sweating and heart complications in diabetic patients, hence, can facilitate diabetes management. Toward this aim, we performed simultaneous monitoring of heart rate, sweat rate, and interstitial fluid (ISF) glucose levels to explore heart and sweat complications during large glucose variation. A diabetic subject wore the microfluidic patch on the finger along with a pulse oximeter. The measurement was done without interrupting the routine insulin injection procedures of the diabetic patient. During the measurement duration, the subject was asked to remain sitting without vigorous movements. ISF glucose data was recorded via Dexcom G6 continuous glucose monitor.
Levodopa Sensing for Parkinson's Disease Management.
Levodopa is a first-line drug for treating Parkinson's disease. It has been reported that long-term intermittent oral dosage of levodopa causes fluctuation in plasma levodopa concentrations and leads to unpredictable responses such as motor fluctuations and dyskinesia; thus, continuous monitoring of levodopa is important to circumvent such unforeseen responses46. Sweat has been reported to contain foreign drugs, including levodopa47,48. Sweat is a promising noninvasive way to continuously monitor levodopa level inside the body. It may also facilitate finding an optimal dosage and interval that is personalized to each patient. In addition, Parkinson's patients usually suffer from abnormal sweating. Hyperhidrosis occurs when the blood levodopa concentration is low8,49 Therefore, studying sweat behavior and monitoring levodopa concentration can assist management of Parkinson's disease. Herein, we conducted on-body trials to study how sweat levodopa evolves within our body. A healthy subject was asked to consume 100 and 200 g intake of broad beans which contain levodopa50 to observe sweat levodopa relation to broad beans intake. In this study, boiled broad beans which were reported to contain approximately 0.6 wt % levodopa were used51. This corresponds to levodopa intake similar to that of levodopa medication consumed by Parkinson's patients in a day. Levodopa sensors were calibrated in sweat as shown in
In summary, we present a wearable device for rapid uptake of nL min−1 cm−2 rates of thermoregulatory sweat at rest, enabling near-real-time sweat rate and composition analysis at rest. This represents a crucial advancement for detecting sweat rates associated with underlying physiological conditions, as demonstrated in subject studies exploring the relation between at-rest sweating and metabolic and stress conditions. Expanding on these preliminary trials, this patch can be deployed for patients or applications where deregulated sweating is a priori known to indicate underlying health conditions or can be used in exploratory subject studies to decode how sweating patterns relate to broader physiology. For example, hypoglycemia is known to qualitatively increase sweating rates as the body seeks to lower core temperature to conserve energy52. The presented patch can be used to more quantitatively study this phenomenon by simultaneously accumulating data on resting sweating rates and blood glucose levels, both for an individual over time and across a population of subjects. Personalized and universal correlations could then be built that enable resting sweat rate to serve as a noninvasive predictor of hypoglycemia. Similarly, excessive sweating is qualitatively known to indicate psychological duress, but more quantitative correlation studies can be performed between resting sweating rate and traditional, invasively obtained or discrete measures of mental state such as cortisol hormone levels53. Based on these correlations, at-rest sweat rate could then be used to continuously and non-invasively estimate stress, with applications in assessing and improving the welfare of infants, soldiers, and stroke patients, and more generally of individuals going about everyday activities. More generally, the presented patch can be used to study correlations between sweat rates and composition, helping to better understand analyte secretion mechanisms and guide how measured concentrations should be interpreted. By allowing these studies to be performed in a way that is compatible with daily routines, this work creates fresh opportunities for decoding how noninvasive parameters relate to deeper body health and for establishing the physiological utility of sweat sensing as a whole.
Materials.
3-Aminopropyltriethoxysilane (APTES), polyvinyl butyral resin BUTVAR B-98 (PVB), aniline, sodium chloride, tyrosinase, glutaraldehyde, bovine serum albumin, thionine acetate salt, NAFION® 117, tetrabutylammonium bromide (TBAB), sodium chloride (NaCl) were purchased from Sigma-Aldrich. Aniline was distilled prior to usage. Silver ink CI-4040 was purchased from EMS Adhesives. Polydimethylsiloxane (Sylgard 184) was purchased from Ellsworth Adhesives. Moisture resistant polyester film 0.0005″ was purchased from McMasterCarr (Los Angeles, Calif.).
Sensor Fabrication.
Conductive Au electrodes were fabricated by standard photolithography and evaporation methods as detailed in our prior work27. Electrochemical depositions required for sensor functionalization were performed on PCI4G300 (Gamry Instruments, USA). pH sensor was prepared by growing Au microstructures at 0 V for 30 s to roughen the surface as demonstrated in previous works54, and then electrochemically depositing aniline solution (1 M HCl, 0.1 M aniline) by performing cyclic voltammetry from −0.2 to 1 V vs. Ag/AgCl at 100 mV/s for 25 cycles. Cl− sensor was prepared by dropcasting silver ink and cured at 90° C. for 30 min. The electrode was subsequently treated with 0.1 M FeCl3 for 1 min. The reference electrode for pH and Cl− sensors was prepared by dropcasting a thin layer of silver ink onto the Au electrode. After drying, a solution containing 79.1 mg PVB and 50 mg NaCl in 1 mL methanol was dropcasted (10 pL/mm2). Levodopa sensor was prepared by initially growing Au nanodendrites using pulsed voltage from −1 to 1 V at a signal frequency of 50 Hz, 50% duty cycle, and 1500 cycles, creating high surface area structures as imaged in our previous work55. Thionine acetate salt solution (0.25 mM) was deposited by applying 1 Hz signal frequency, pulsed voltage from −0.6 to 0 V, 90% duty cycle, and 660 cycles. Next, 0.2 pL of Tyrosinase solution containing 99 μL of 1% bovine serum albumin, 1 μL of 2.5% glutaraldehyde, and 0.25 μL of 1 mg/mL tyrosinase was dropcasted and dried. The membrane was additionally coated with 0.2 μL of NAFION-TBAB solution which was prepared as reported in literature56. The levodopa sensors could be used after drying for an 30 hour at room temperature. For longterm storage, levodopa sensors were kept at 4° C. The shared reference/counter electrode for levodopa sensor was prepared by dropcasting silver ink and letting it dry before usage.
Microfluidic Device Fabrication.
Microfluidic was fabricated using standard photolithography process. SU8 photoresist was used to pattern microfluidics on a Si wafer. PDMS (base to curing agent ratio of 10:1) was poured onto the SU8 mold and cured at 60° C. for 4-5 h. The cured PDMS was peeled off and put under O2 plasma, along with the PET patterned with sensing electrodes at a power of 90 W, 0.2 mtorr for 1 min. 1% APTES solution was dropcasted on entire surface of the PET for 2 min. The PET was cleaned with DI water and quickly dry with N2. The PET was then bonded with PDMS and left it for at least an hour before usage. PDMS is soaked in DI water for 5 h prior to utilization to saturate PDMS57 such that permeation-driven now is minimized58. Oversaturation can also be achieved through longer presoak time at high temperature. By presoaking, sweat-containing microfluidic channel evaporated/diffused through the PDMS at 0.01 nL min−1 cm−2 when the device was tested for 8 h at 21-23° C. and relative humidity of 3942%.
Hydrophilic Filler Fabrication.
The patterned SU8 filler was prepared to a thickness of 200 μm on a flexible PET using standard procedures. The filler was carefully peeled off from the PET and put under O2 plasma. A solution containing 0.5% PVA in DI water was then drop-casted onto the filler (0.5 μL/mm2), ensuring a complete coverage on the entire filler (including side and back walls), and was quickly heated on a hotplate at 80° C. The PVA film was approximately 10 μm in thickness. Once PVA dried, an AG-GLY film was placed on top of the filler. AG− GLY film was prepared by stirring and dissolving 2% agarose and 50% glycerol in DI water at 120° C. for 5 min. Once everything dissolved, ˜3 mL of the solution was quickly poured into a 100 mm hydrophilic glass dish and waited until the solution dried to become a gel-like film. The AG-GLY solution is viscous and dries easily; hence, rapid pour on a hydrophilic dish is necessary for a thin and uniform thickness. Here the AG-GLY film was not directly drop-casted on the filler because of the difficulty to achieve a thin uniform coating on the entire filler if we directly drop-casted the solution. The AG-GLY film was saturated with deionized water before placing on the filler. The film is approximately 90-130 μm thick. The laminated filler was finally placed inside the collection well of the microfluidic patch.
Device Characterization.
Sensor characterizations were performed on CHI1430 (CH Instruments, USA). The pH sensor was tested using McIlvaine's buffer of pH 4-8, and Cl− sensor was tested using NaCl solution of concentration ranging from 25 to 200 mM. The potential difference with respect to a reference electrode was measured for both sensors. Levodopa sensor was measured by applying 0.35 V with respect to a shared reference/counter silver electrode. Flow rate experiments were carried out using Harvard Apparatus PHD 2000 Syringe Pump.
On-body sweat analysis. On-body human trials were carried out at the University of California, Berkeley in compliance with the human research protocol (CPHS 2014-08-6636 and CPHS 2015-05-7578) approved by the Berkeley Institutional Review Board (IRB). Both male and female subjects (between aged 21 and 45) were recruited from the Berkeley campus through campus flyers and verbal recruitments. Informed consents were obtained from all study subjects before enrollment in the study. The trials indicated in
Statistical Analysis.
Standard deviations shown in Fig. bookmark 12 5, panel c and reported in Table 2 are calculated by considering multiple measurements of instantaneous sweat rate at each tested body location.
Rationale for Microfluidic Channel Dimensions
The sweat gland is treated as a volumetric fluid source generating sweat at rate Q and exiting the sweat gland with secretory pressure Pg. This sweat is forced into the device with hydraulic resistance Rtot=Rwell+Rchannel, producing a pressure drop of ΔP=RtotQ. To sustain this flow in the device, Pg must be larger than ΔP (ignoring atmospheric and Laplace pressures). Pg higher than this required pressure does not change the flow rate Q in the device but instead means that sweat will exit the microfluidic channel with some nonzero pressure.
Hydraulic resistance of the channel is given by
R
channel=12 μL/[0.37*w4]
where L=15 cm, w=70 um, and μ=viscosity=9.5*10−4 Pa-s.2 By Darcy's law,
R
well=μL/kA=2.17*1013 Pa-s/m3
where k=Darcy permeability for 2% agarose hydrogels of 100 μm thickness≈620 nm2,3 L=100 μm, and A=cross sectional area of 3-mm diameter well.
Secretory pressures of the sweat gland for exercise and sauna-induced sweat are around 2.5 kPa, while those of chemically induced sweat can reach upwards of 70 kPa.1,4 Using the lower pressure as a conservative estimate, we can scale it down to lower resting sweat volumes (drawing on proportionalities indicated by Hoff's law P=σRTΔC since we expect the osmolality gradient to be related to secretion rate) assuming 2.5 kPa pressure corresponds conservatively to high rates of 20 nL min−1 gland−1. Then at extreme resting sweat rates of 1 nL min−1 cm−2 in a 3-mm diameter well, corresponding to 70.7 nL min−1 entering the device, we can compare the estimated sweat gland secretory pressure of 1.2 kPa to the hydraulic pressure drops associated with different channel geometries to arrive at optimal dimensions (see, e.g.,
Choosing a channel width and height of 70 μm and a length around 15 cm allows a large enough volume capacity as well as a cross sectional area that is small enough to ensure fast sweat speed in the channel (necessary for high-resolution sweat rate measurement) but large enough to avoid excessive hydraulic pressure losses. In this case Rtot=Rwell+Rchannel=1.92*1014+2.17*1013 Pa-s/m3=2.1*1014 Pa-s/m3. ΔP is calculated for a broad range of resting sweat secretion and flow rates (high, medium, and low) and compared to the secretory pressure expected at those flow rates (according to P=2.5 kPa*Q/(20 nL min−1 gland−1)) (see, e.g. Table 3, in a 3-mm diameter region with 7 glands based on typical sweat gland densities of 100 glands cm−2) to confirm that the gland is a sufficient pump to inject sweat into a device of these dimensions.
Impact of Taylor Dispersion on Sensor Lag Times and Accuracy
Analyte diffusivity, sweat collection volume, and sweat secretion rate will impact the time lag between when sweat of a certain composition is secreted and when it is registered by the sensor. To estimate this, we consider sweat mixing and Taylor dispersion in the collection well and channel respectively using extremes of the above parameters. The following considerations are applied in our simulations:
1) The effective volume of the hydrogel-containing collection well is 72 nL for a 3 mm-diameter region. Because of the large-area proportions of the collection well, there is bulk mixing between older and fresher sweat that is treated as a continual averaging in the well.
2) We consider three sweat secretion rates (high—1000 nL min−1 cm−2, medium—50 nL min−1 cm−2, and low—3 nL min−1 cm−2) that encompass a broad range of resting sweating rates. We consider sweat collection only in the 3 mm-diameter well as this broad range encompasses rates expected with the larger 8 mm opening. We consider the channel with cross section of 70 μm×70 μm.
3) Diffusivities of H+, Cl−, and levodopa fall between 1 and 10 (×10−9) m2/s in water and in the agarose hydrogel, so these extreme values are used in the simulations.5,6
4) The concentration of sweat at the sensor position depends on older sweat deeper in the channel and on sweat upstream in the channel and well. Sensor accuracy thus depends on the specific sweat composition profile, but to give a general sense of the time lags involved we consider a step concentration profile in which sweat entering the channel has concentration 0.5 for t<0 and concentration of 1 at t≥0. We solve the diffusion-advection equation in 1D (along the channel length) while incorporating Taylor dispersion to consider the temporal accuracy with which our device can reconstruct this concentration profile.
Microchannel:
In the channel, the plots in
Collection Well:
In the collection well, the plot below averages sweat at concentration 0.5 before t=0 with subsequent secretion of sweat at concentration 1 for t>0 for the three sweat rates. Table 5 below captures the time lag between when sweat at concentration 1 starts secreting and when the well captures 90% of the full change in concentration (see,
Overall, mixing and Taylor dispersion through the sections of the device indicate that at relatively high resting sweating rates on the fingertips, the sensor has a lag of around 3 minutes between when sweat at a certain composition is secreted and when it is detected at the sensor.
It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. All publications, patents, and patent applications cited herein are hereby incorporated by reference in their entirety for all purposes.
This application claims priority to and benefit of U.S. Ser. No. 63/013,315, filed Apr. 21, 2020, which is incorporated herein by reference in its entirety for all purposes.
This invention was made with government support under Grant Number 1160494 awarded by the National Science Foundation. The government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/028244 | 4/20/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63013315 | Apr 2020 | US |
