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Potential signs of life on Venus are fading fast

Science

The announcement in September took the world by storm: In radio emissions from Venus's atmosphere, researchers found signs of phosphine, a toxic compound that on Earth is made in significant amounts only by microbes and chemists. The unexpected detection could point to a microbial biosphere floating in the venusian clouds, the researchers suggested in Nature Astronomy . But almost immediately, other astronomers began to point out questionable methods or said they couldn't reproduce results. Now, after reanalyzing their data, the original proponents are downgrading their claims. Phosphine levels are at least seven times lower than first claimed, the authors reported in a preprint posted on 17 November to arXiv. But the team still believes the gas is there, Jane Greaves, an astronomer at Cardiff University who led the work, said in a talk last week to a NASA Venus science group. “We have again a phosphine line.” The original observations were made in 2017 at the James Clerk Maxwell Telescope (JCMT) in Hawaii, and in 2019 at the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile. In Venus's radio spectrum, Greaves and her colleagues detected an absorption line they attributed to phosphine. The researchers went to great lengths to remove confounding effects such as absorption by Earth's own atmosphere. But critics said such aggressive fixes made the discovery of a false positive more likely. ALMA scientists have since found a new noise source: telescope calibration errors. After reanalyzing the ALMA data, Greaves said her team now finds phosphine at just 1 part per billion (ppb). That's still above levels that can be explained by natural processes such as volcanic eruptions or lightning strikes, Greaves said. A study published last month in Astronomy & Astrophysics , led by Therese Encrenaz, an astronomer at the Paris Observatory, ruled out higher phosphine levels. Her team analyzed observations made in 2015 by NASA's Infrared Telescope Facility in Hawaii. Phosphine should have popped out if it had existed at levels above 5 ppb. “It's easy to see there's no phosphine line,” Encrenaz says. If the line does exist, it might not be due to phosphine, according to a critique submitted to Nature Astronomy . It argues that the dip in the JCMT spectrum can be explained by an overlapping absorption line from sulfur dioxide (SO2), the gas that makes up most venusian clouds. The Greaves team concedes the point in its reanalysis. “We emphasize that there could be a contribution from SO2,” they write. But the width of the absorption line in the ALMA data suggests the feature isn't “solely SO2,” they write. Just where any signal is coming from is also in dispute. ALMA is only sensitive to absorption from substances at altitudes above 70 kilometers (km), Encrenaz says. But the Nature Astronomy paper suggested the signal originated some 55 km up, in warmer, more hospitable cloud layers. “This is very difficult to conceive,” Encrenaz says. Greaves and her co-authors argue in their reanalysis that ALMA is unable to capture the full width—and therefore depth—of the signal. “There is no empirical evidence that [phosphine] lies only above 70 km.” Colin Wilson, a co-author of the Nature Astronomy critique, says it's too early to say where the “phosphine roller coaster will end up.” More observations at ALMA might settle the issue, he says. “Whether or not we find phosphine, we're likely to find something new.”


Versatile building blocks make structures with surprising mechanical properties

Robohub

Researchers at MIT's Center for Bits and Atoms have created tiny building blocks that exhibit a variety of unique mechanical properties, such as the ability to produce a twisting motion when squeezed. These subunits could potentially be assembled by tiny robots into a nearly limitless variety of objects with built-in functionality, including vehicles, large industrial parts, or specialized robots that can be repeatedly reassembled in different forms. The researchers created four different types of these subunits, called voxels (a 3D variation on the pixels of a 2D image). Each voxel type exhibits special properties not found in typical natural materials, and in combination they can be used to make devices that respond to environmental stimuli in predictable ways. Examples might include airplane wings or turbine blades that respond to changes in air pressure or wind speed by changing their overall shape. The findings, which detail the creation of a family of discrete "mechanical metamaterials," are described in a paper published in the journal Science Advances, authored by recent MIT doctoral graduate Benjamin Jenett PhD '20, Professor Neil Gershenfeld, and four others.


Spectroscopy and Chemometrics News Weekly #47, 2020

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NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 46, 2020 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry foodindustry Analysis Lab Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Get the Spectroscopy and Chemometrics News Weekly in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Near infrared absorption spectroscopy for the quantification of unsulfated alcohol in sodium lauryl ether sulfate" LINK "Estimation of Organic Carbon in Anthropogenic Soil by VIS-NIR Spectroscopy: Effect of Variable Selection" LINK "Near infrared spectroscopy (NIRS) based high-throughput online assay for key cell wall features that determine sugarcane bagasse digestibility") LINK "Authentication of barley-finished beef using visible and near infrared spectroscopy (Vis-NIRS) and different discrimination approaches" LINK "Energetic Distribution of States in Irradiated Low-Density ...


Machine-learning software competes with human experts to optimise organic reactions

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A free software tool that can find the best conditions for organic synthesis reactions often does as well as expert chemists – somewhat to the surprise of the researchers. The software, called LabMate.ML, suggests a random set of initial conditions – such as the temperature, the amount of solvent and the reaction time – for a specific reaction, with the aim of optimising its yield. After those initial reactions are carried out by a human chemist, their resulting yields are read with nuclear magnetic resonance and infrared spectroscopy, digitised into binary code and then fed back into the software. LabMate.ML then uses a machine-learning algorithm to make decisions about the yields, and then recommends further sets of conditions to try. Researcher Tiago Rodrigues of the University of Lisbon says LabMate.ML usually takes between 10 and 20 iterations to find the greatest yield, while the number of initial reactions varies between five and 10, depending on how many conditions are being optimised.


EPA Kicks Off America Recycles Week with Second Annual Innovation Fair

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This week, the U.S. Environmental Protection Agency (EPA) celebrates America Recycles Week by hosting two days of free, virtual events that focus on creating a more robust and sustainable recycling system in the U.S. and abroad. Today, the America Recycles: Innovation Fair will feature more than 40 innovators from across the recycling system via virtual exhibit halls demonstrating their state-of-the-art products, services, outreach, and technologies. They are advancing the recycling system through strategies such as: deploying artificial intelligence robots to enhance operations at recycling facilities; using hard-to-recycle plastics in 3D printing materials; installing small system sorting units in stadiums and small communities; creating new construction materials from hard-to-recycle plastics; and using automated technology and recycled glass bottles to create new glassware. "EPA is proud to showcase top recycling innovators at the virtual Innovation Fair today," said EPA Administrator Andrew Wheeler. "Tomorrow's America Recycles Summit will include EPA's announcement of the first National Recycling Goal, which will prompt a whole new level of dialogue among stakeholders on how to improve our domestic recycling infrastructure."


The more and less of electronic-skin sensors

Science

Electronic skins (e-skins) are flexible electronic devices that emulate properties of human skin, such as high stretchability and toughness, perception of stimuli, and self-healing. These devices can serve as an alternative to natural human skin or as a human-machine interface ([ 1 ][1]–[ 3 ][2]). For on-skin applications, an e-skin should be multimodal (sense more than one external stimulus), have a high density of sensors, and have low interference with natural skin sensation. On pages 961 and 966 of this issue, You et al. ([ 4 ][3]) and Lee et al. ([ 5 ][4]), respectively, report advances of skin-like electronic devices. You et al. present a stretchable multimodal ionic-electronic (IE) conductor–based “IEM-skin” that can measure both strain and temperature inputs without signal interference. Lee et al. describe an ultrathin capacitive pressure sensor based on conductive and dielectric nanomesh structures that can be attached to a human fingertip for grip pressure and force measurement without affecting natural skin sensation. The human skin contains a large number of mechanoreceptors and thermoreceptors (nerve endings that sense deformation and temperature, respectively) that provide distinct perception of the spatial distributions of strain and temperature on our skin induced by touch stimulations ([ 6 ][5]). To replicate these sensory functions of the natural skin, different types of sensors that act as artificial receptors are integrated onto an e-skin for multimodal sensation ([ 7 ][6]). However, an e-skin containing a high-density array of sensory “pixels” of different types for sensing different physical quantities tends to have a complex structure and is challenging to manufacture. A preferred strategy for realizing multimodal sensation on an e-skin is to use the same sensory unit for detecting different physical quantities without signal interference, an approach called decoupled multimodal sensing. Traditional stretchable sensors are sensitive to both strain and temperature and cannot be used as artificial multimodal receptors without signal interference. Targeting interference-free strain and temperature sensing by a single sensory unit, You et al. creatively used the ion relaxation dynamics of an ion conductor (an elastomer mixed with an ionic liquid) to decouple the strain and temperature measurement and developed the IEM-skin composed of an array of artificial multimodal ionic receptors. They fabricated the IEM-skin by sandwiching a thin layer of ion conductor with two layers of orthogonally patterned stretchable electrode strips (see the figure, top ). A pixelated matrix of millimeter-sized artificial receptors formed between the top and bottom electrodes. The electrical properties of each receptor are affected by the externally applied strain and temperature stimuli and can be measured through impedance measurement. You et al. used a strain-independent intrinsic electrical parameter of the ion conductor, the charge relaxation time, which reflects the ionic charge dynamics of the ion conductor and is equal to the ratio of material's dielectric constant and ion conductivity ([ 8 ][7], [ 9 ][8]). The charge relaxation time is the signal readout for temperature and is not affected by the deformation of the IEM-skin. For strain measurement, the bulk capacitance of the ion conductor is measured. The effect of temperature on the capacitance is eliminated through normalization against a reference capacitance at the temperature measured by the receptor. Thus, an external strain input only changes geometric parameters of the ion conductor, whereas a temperature input primarily modulates the intrinsic electrical properties (dielectric constant and ion conductivity) of the ion conductor. Another enabling factor of the IEM-skin design is its emulation of the epidermis and dermis bilayer of the human skin by suspending the receptor matrix layer over a low-friction interface layer filled with talcum powder. This design allows three-dimensional wrinkle-like deformations of the IEM-skin under different contact modes (such as shear, pinch, tweak, and torsion) and permits the IEM-skin to distinguish these contact modes through the measured temperature and strain profiles. Data confirm that the IEM-skin can perform decoupled, real-time measurement of strain and temperature with high accuracy. The IEM-skin can serve as a human-machine interface that accepts tactile inputs of different contact modes and can be integrated into prosthetic and robotic devices to provide tactile and thermal feedback with high spatial resolution. The concept of using intrinsic electrical parameters, such as conductivity and dielectric constant of sensing materials, for strain-independent temperature sensing can be generalized to developing other types of stretchable multimodal sensors for humidity, chemicals, and biomolecules. One limitation is that the method for recognizing different tactile input modes through the measured temperature and strain profiles only works for interactions with hot or cold objects at temperatures different from that of the IEM-skin. Alternative solutions may include the use of learning-based recognition models purely based on strain-distribution data or modulation of the temperature of the IEM-skin (by adding a heating layer) based on the environment. Skin-like electronic sensors also hold great potential for construction of hand-wearing devices such as instrumented gloves for quantifying tactile signals like force and pressure during finger or in-hand manipulation ([ 10 ][9]). Such data could facilitate the decoding of human hand sensation and its roles in object manipulation and enable better designs of robotic and prosthetic hands with biomimetic sensory feedback ([ 11 ][10]). Targeting imperceptible wearing and tactile sensing on fingertips, Lee et al. developed an ultrathin capacitive pressure sensor consisting of multilayers of conductive and dielectric nanomesh structures. This sensor design is derived from the design of conductive nanomesh electrodes proposed by Miyamoto et al. ([ 12 ][11]), which can be directly laminated on human skin during fabrication. The electrode is fabricated by first electrospinning a water-soluble polymer, polyvinyl alcohol (PVA) into a multilayered mesh-like network of 300- to 500-nm-wide nanofibers. A 100-nm-thick gold layer is then deposited onto the PVA nanomesh sheet, and the gold-coated nanomesh sheet is transferred onto the skin surface. The sacrificial PVA nanofibers are washed off by water, but a residual layer of the dissolved PVA greatly facilitates the attachment of the resultant gold nanomesh layer onto the textured skin surface with excellent adhesion and conformal contact. The skin-integrated nanomesh electrode is stretchable and highly breathable and has exceptionally low bending stiffness, and so it creates no mechanical constraint or dermatological irritation to the skin. To fabricate a nanomesh pressure sensor (see the figure, bottom), Lee et al. first laminated a nanomesh electrode on the skin surface and then sequentially attached a dielectric nanomesh layer made of electrospun polyurethane and parylene nanofibers and another nanomesh electrode layer to form a parallel-plate capacitor structure. Then, a nanomesh passivation layer of polyurethane nanofibers was attached to the top electrode layer with dissolved PVA nanofibers as the filler and adhesive. The total thickness of the nanomesh pressure sensor is ∼13 μm. When fingers wearing such a pressure sensor grip an object, the grip force applied to the pressure sensor deforms the middle dielectric nanomesh layer and leads to a change in the capacitance measured between the top and bottom electrodes as the sensor readout. ![Figure][12] Improved electronic skins Two goals in artificial touch sensors are to sense more than one stimulus with one receptor and to create wearable sensors that maintain natural skin sensation. GRAPHIC: C. BICKEL/ SCIENCE Through object-gripping experiments performed by human participants, Lee et al. investigated the effect of the finger-integrated pressure sensor on the natural fingertip sensation and found no decrease of the sensory feedback caused by the attachment of the pressure sensor. They hypothesized that the ultrathin and compliant structure of the nanomesh pressure sensor renders the device imperceptible on the fingertip. In addition, the intimate and conformal adhesion of the sensor's bottom nanomesh electrode layer to the skin surface may also contribute to the negligible interference of the finger skin sensation by the sensor attachment. This sensor also shows excellent mechanical durability under cyclic compression, shearing, and surface friction, which is attributed to the high mechanical robustness of the multilayered nanomesh structure of the pressure sensor. This work highlights another new application of the previously reported skin-integrated nanomesh electronics ([ 12 ][11]) to wearable physical sensing with unprecedented performance. Future work may involve the further examination of fundamental mechanisms for the on-skin imperceptibility of the nanomesh pressure sensor, the systematic study of the skin-integrated pressure sensor performance for grasping objects of different materials and properties (such as insulating versus conductive, hard versus soft, and smooth versus textured), and the scalable fabrication of pixelated nanomesh pressure sensors in a large area with high density. The nanomesh pressure sensor could record tactile signals of human-hand manipulation that could provide superior sensing performance and zero data artifacts over existing instrumented gloves and e-skins. Multimodal sensation and nonobstructive skin integration are two important features that are desirable in e-skin designs. The studies reported by You et al. and Lee et al. , respectively, provide new solutions to better realize these attractive features with simplified device structures and enhanced sensing performance without impeding natural sensation. These results will inspire new sensor designs and lead to applications of e-skins as wearable health care monitoring, sensory prosthetic and robotic devices, and high-performance human-machine interfaces. 1. [↵][13]1. J. C. Yang et al ., Adv. Mater. 31, 1904765 (2019). [OpenUrl][14] 2. 1. T. R. Ray et al ., Chem. Rev. 119, 5461 (2019). [OpenUrl][15] 3. [↵][16]1. T. Someya, 2. M. Amagai , Nat. Biotechnol. 37, 382 (2019). [OpenUrl][17][CrossRef][18][PubMed][19] 4. [↵][20]1. I. You et al ., Science 370, 961 (2020). [OpenUrl][21][CrossRef][22] 5. [↵][23]1. S. Lee et al ., Science 370, 966 (2020). [OpenUrl][24][CrossRef][25] 6. [↵][26]1. A. Zimmerman, 2. L. Bai, 3. D. D. Ginty , Science 346, 950 (2014). [OpenUrl][27][Abstract/FREE Full Text][28] 7. [↵][29]1. S. Jeon, 2. S.-C. Lim, 3. T. Q. Trung, 4. M. Jung, 5. N.-E. Lee , Proc. IEEE 107, 2065 (2019). [OpenUrl][30] 8. [↵][31]1. C. Gainaru et al ., J. Phys. Chem. B 120, 11074 (2016). [OpenUrl][32][CrossRef][33][PubMed][34] 9. [↵][35]1. B. A. Mei, 2. O. Munteshari, 3. J. Lau, 4. B. Dunn, 5. L. Pilon , J. Phys. Chem. C 122, 194 (2018). [OpenUrl][36] 10. [↵][37]1. S. Sundaram et al ., Nature 569, 698 (2019). [OpenUrl][38][CrossRef][39][PubMed][40] 11. [↵][41]1. E. D'Anna et al ., Sci. Robot. 4, eaau8892 (2019). [OpenUrl][42] 12. [↵][43]1. A. Miyamoto et al ., Nat. Nanotechnol. 12, 907 (2017). [OpenUrl][44][CrossRef][45][PubMed][46] Acknowledgments: X.L. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2017-06374). 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On Dollar Slices, Pizza Vectors, Prosciutto Zones and Topping Hyperspace

#artificialintelligence

At Topos, we are fascinated by exactly this type of variation and believe it provides a powerful view into the culture of a location. While data sources like the United States Census are useful for understanding broad demographic trends over decades, they give little insight into what defines the moment-to-moment culture of a city, a neighborhood, a street corner. Inspired by thinkers like Walter Benjamin, who, in his unfinished Arcades Project examined subjects as varied as fashion, construction materials, poetry, lighting, and mirrors in order to understand Paris in the 19th century, we are fascinated by the way seemingly simple, ubiquitous subjects like the coffee we drink or the concerts we go to define a place. However, unlike Benjamin, we are interested in constructing this understanding in a way that can dynamically scale across the globe, allowing us to understand how different locations relate to one another, and how locations evolve in real time. To achieve this, we use data from dozens of different sources and techniques from a wide range of technologies and disciplines including computer vision, natural language processing, statistics, machine learning, network science, topology, architecture and urbanism.


Windfall Geotek (TSXV: WIN)

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Windfall Geotek (formerly Albert Mining) is a Canadian corporation offering a proven and industry-leading digital platform leveraging Artificial Intelligence (AI) technologies to significantly improve outcomes in the exploration, development, operations and financing of geologically focused projects. Principal markets encompass the global resource mining industry including virtually all forms of mineralization including oil and gas exploration. Recent advances have led to the detection of water sources and aquifers especially in drought regions, and of anti-personnel landmines and related deadly legacy hazards in conflict zones. Our applied machine learning technology offers a revolutionary approach to geologic discovery and a markedly positive economic impact on operational efficiencies. Since 2004 our Company has added value to over 30 client discoveries and more than 80 target generation projects around the globe.


Predictive AI Provides Update on Sales Process and Pipeline

#artificialintelligence

Predictiv AI Inc. (TSX VENTURE: PAI) (OTC: INOTF) (FRANKFURT: 71TA) ("Predictiv AI" or the "Company"), www.predictiv.ai, a software and solutions provider in the artificial intelligence and industrial IoT markets, is pleased to provide a sales update on ThermalPass www.thermalpass.com, Predictiv AI is delivering units, against five initial orders, over the next 45 days. The initial customer base consists of a wide cross-section of businesses and organizations which include a convention center, a hospital, a mining company, two manufacturing plants and a leading industrial conglomerate. The internal sales and marketing team has also built an extensive pipeline since ThermalPass' commercial launch 30 days ago. In addition, the Company has entered into seven strategic reseller contracts with established sales channels in Canada, the United States and Europe.


Researchers hack a robotic vacuum cleaner to record speech remotely

Daily Mail - Science & tech

Scientists have found that robotic vacuum cleaners could allow snoopers to remotely listen in to household conversations, despite not being fitted with microphones. US experts found they can perform a remote eavesdropping attack on a Xiaomi Roborock robot cleaner by remotely accessing its Lidar readings – which helps these cleaners to avoid bumping into furniture. Lidar is a method for measuring distances by illuminating the target with laser beams and measuring their reflection with a sensor. But Lidar can also capture sound signals by obtaining reflections off of objects in the home, like a rubbish bin, that vibrate due to nearby sound sources, such as a person talking. A hacker could repurpose a vacuum's Lidar sensor to sense acoustic signals in the environment, remotely harvest the Lidar data from the cloud and process the raw signal with deep learning techniques to extract audio information.