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Natural and anthropogenic biomass burning are among the major sources of particulate pollution worldwide that affects air quality, climate and human health. Delhi, one of the world’s most populated cities, experiences severe haze events caused by particulate pollution during winter, but the underlying pathways remain poorly understood. Here we observe intense and frequent nocturnal particle growth events during haze development in Delhi from measurements of aerosols and gases during January–February at the Indian Institute of Technology in Delhi. The particle growth events occur systematically despite the unfavourable condition for new-particle formation, including the lack of photochemical production of low-volatility vapours and considerable loss of vapours under extremely polluted conditions. We estimate that this process is responsible for 70% of the total particle-number concentration during haze. We identify that the condensation of primary organic vapours from biomass burning is the leading cause of the observed growth. The sharp decrease in night-time temperatures and rapid increase in biomass-burning emissions drive these primary organic vapours out of equilibrium, resulting in their condensation and the growth of nanoparticles into sizes relevant for haze formation. This high impact of primary biomass-burning emissions on night-time nanoparticle growth is unique compared with most urban locations globally, where low-volatility vapours formed through oxidation during the day drive particle growth and haze formation. As uncontrolled biomass burning for residential heating and cooking is rife in the Indo–Gangetic plain, we expect this growth mechanism to be a source of ultrafine particles, affecting the health of 5% of the world’s population and impacting the regional climate. Our work implies that regulating uncontrolled biomass-combustion emissions may help inhibit nocturnal haze formation and improve human health in India.

We obtain algorithmically effective versions of the dense lattice sphere packings constructed from orders in $\mathbb {Q}$ -division rings by the first author. The lattices in question are lifts of suitable codes from prime characteristic to orders $\mathcal {O}$ in $\mathbb {Q}$ -division rings and we prove a Minkowski–Hlawka type result for such lifts. Exploiting the additional symmetries under finite subgroups of units in $\mathcal {O}$ , we show that this leads to effective constructions of lattices approaching the best known lower bounds on the packing density $\Delta _{n}$ in a variety of new dimensions $n$ . This unifies and extends a number of previous constructions.

We demonstrate the use of electric fieldsto rapidly form gels of the biopolymer alginate (Alg) in specific three-dimensional (3-D) shapes and patterns. In our approach, we start with a gel of the biopolymer agarose, which is thermoresponsive and hence can be molded into a specific shape. The agarose mold is then loaded with Ca2+cations and placed in a beaker containing an Alg solution. The inner surface of the beaker is surrounded by aluminum foil (cathode), and a copper wire (anode) is stuck in the agarose mold. These are connected to a direct current (DC) power source, and when a potential of ∼10 V is applied, an Alg gel is formed in a shape that replicates the mold. Gelation occurs because the Ca2+ions electrophoretically migrate away from the mold, whereupon they cross-link the Alg chains adjacent to the mold. At low Ca2+(0.01 wt %), the Alg gel layer grows outward from the mold surface at a steady rate of about 0.8 mm/min, and the gel stops growing when the field is switched off. After a gel of desired thickness is formed, the agarose mold can be melted away to leave behind an Alg gel in a precise shape. Alg gels formed in this manner are transparent and robust. This process is particularly convenient to form Alg gels in the form of hollow tubes, including tubes with multiple concentric layers, each with a different payload. The technique is safe for encapsulation of biological species within a given Alg layer. We also create Alg gels in specific patterns by directing gel growth around selected regions. Overall, our technique enables lab-scale manufacturing of alginate gels in 3-D without the need for an expensive 3-D printer.

ABSTRACTPM2.5sampling was conducted at a curbside location in Delhi city for summer and winter seasons, to evaluate the effect of PM2.5and its chemical components on the visibility impairment. The PM2.5concentrations were observed to be higher than the National Ambient Air Quality Standards (NAAQS), indicating poor air quality. The chemical constituents of PM2.5(the water-soluble ionic species SO42-, NO3−, Cl−, and NH4+, and carbonaceous species: organic carbon, elemental carbon) were analyzed to study their impact on visibility impairment by reconstructing the light extinction coefficient, bext. The visibility was found to be negatively correlated with PM2.5and its components. The reconstructed bextshowed that organic matter was the largest contributor to bextin both the seasons which may be attributed to combustion sources. In summer season, it was followed by elemental carbon and ammonium sulfate; however, in winter, major contributions were from ammonium nitrate and elemental carbon. Higher elemental carbon in both seasons may be attributed to traffic sources, while lower concentrations of nitrate during summer, may be attributed to volatility because of higher atmospheric temperatures.Implications: The chemical constituents of PM2.5that majorly effect the visibility impairment are organic matter and elemental carbon, both of which are products of combustion processes. Secondary formations that lead to ammonium sulfate and ammonium nitrate production also impair the visibility.

External triggers such as pH or temperature can induce hydrogels to swell or shrink rapidly. Recently, these triggers have also been used to alter the three-dimensional (3-D) shapes of gels: for example, a flat gel sheet can be induced to fold into a tube. Self-folding gels are reminiscent of natural structures such as the Venus flytrap, which folds its leaves to entrap its prey. They are also of interest for applications in sensing or microrobotics. However, to advance the utility of self-folding gels, the range of triggers needs to be expanded beyond the conventional ones. Toward this end, we have designed a class of gels that change shape in response to very low concentrations of specific biomolecules. The gels are hybrids of three different constituents: (A) polyethylene glycol diacrylate (PEGDA); (B) gelatin methacrylate-co-polyethylene glycol dimethacrylate (GelMA-co-PEGDMA); and (C) N-isopropylacrylamide (NIPA). The thin-film hybrid is constructed as a bilayer or sandwich of two layers, with an A/B layer (alternating strips of A and B) sandwiched above a layer of gel C. Initially, when this hybrid gel is placed in water, the C layer is much more swollen than the A/B layer. Despite the swelling mismatch, the sheet remains flat because the A/B layer is very stiff. When collagenase enzyme is added to the water, it cleaves the gelatin chains in B, thus reducing the stiffness of the A/B layer. As a result, the swollen C layer is able to fold over the A/B layer, causing the sheet to transform into a specific shape. The typical transition is from flat sheet to closed hollow tube, and the time scale for this transition decreases with increasing enzyme concentration. Shape transitions are induced by enzyme levels as low as 0.75 U/mL. Interestingly, a shape transition is also induced by adding the lysate of murine fibroblast cells, which contains enzymes from the matrix metalloproteinase (MMP) family at levels around 0.1 U/mL (MMPs are similar to collagenase in their ability to cleave gelatin). We further show that transitions from flat sheets to other shapes such as helices and pancakes can be engineered by altering the design pattern of the gel. Additionally, we have made a rudimentary analog of the Venus flytrap, with two flat gels (“leaves”) flanking a central folding gel (“hinge”). When enzyme is added, the hinge bends and brings the leaves together, trapping objects in the middle.

We report the design of hydrogels that can act as “smart” valves or membranes. Each hydrogel is engineered with a pore (about 1 cm long and <1 mm thick) that remains closed under ambient conditions but opens under specific conditions. Our design is inspired by the stomatal valves in plant leaves, which regulate the movement of water and gases in and out of the leaves. The design features two different gels, active and passive, which are attached concentrically to form a disc-shaped hybrid film. The pore is created in the central active gel, and the conditions for opening the pore can be tuned based on the chemistry of this gel. For example, if the active gel is made from N-isopropylacrylamide (NIPA), the actuation of the pore depends on the temperature of water relative to 32 °C, which is the lower-critical solution temperature (LCST) of NIPA. The concentric design of our hybrid provides directionality to the volumetric transition of the active gel, i.e., it ensures that the pore opens as the active gel shrinks. In turn, contact with hot water (T> 32 °C) opens the pore and allows the water to pass through the gel. Conversely, the pore remains closed when the water is cold (T< 32 °C). The gel thereby acts as a “smart” valve that is able to regulate the flow of solvent depending on its properties. We have extended the concept to other stimuli that can cause gel-swelling transitions including solvent composition, pH, and light. Additionally, when two different gel-based valves are arranged in series, the assembly acts as a logical “AND” gate, i.e., water flows through the valve-combination only if it simultaneously satisfies two distinct conditions (such as its pH being below a critical value and its temperature being above a critical value).

We describe the creation of polymeric microcapsules that can exhibit autonomous motion along defined trajectories. The capsules are made by cross-linking aqueous microdroplets of the biopolymer chitosan using glutaraldehyde. A coflow microfluidic tubing device is used to generate chitosan droplets containing nanoparticles (NPs) with an iron (Fe) core and a platinum (Pt) shell. The droplets are then incubated in a Petri dish with the cross-linking solution, and an external magnet is placed below the Petri dish to pull the NPs together as a collective “patch” on one end of each droplet. This results in cross-linked capsules (∼150 μm in diameter) with an anisotropic (patchy) structure. When these capsules are placed in a solution of H2O2, the Pt shell of the NPs catalyzes the decomposition of H2O2into O2gas, which is ejected from the patchy end in the form of bubbles. As a result, the capsules (which are termed micromotors) move in a direction opposite to the bubbles. Furthermore, the micromotors can be steered along specific paths by an external magnet (the magnetic response arises due to the Fe in the core of the NPs). A given micromotor can thus be directed to meet with and adhere to an inert capsule, i.e., a model cargo. Adhesion occurs due to the soft nature of the two structures. Once the cargo is picked up, the micromotor-cargo pair can be moved along a specific path to a destination, whereupon the cargo can be released from the micromotor. We believe these soft micromotors offer significant benefits over their existing hard counterparts because of their biocompatibility, biodegradability, and ability to encapsulate a variety of payloads.

We demonstrate the use of electroadhesion (EA), i.e., adhesion induced by an electric field, to connect a variety of soft materials into 3D structures. EA requires a cationic and an anionic material, but these can be of diverse origin, including covalently cross-linked hydrogels made by polymerizing charged monomers or physical gels/capsules formed by the ionic cross-linking of biopolymers (e.g., alginate and chitosan). Between each cationic/anionic pair, EA is induced rapidly (in ∼10 s) by low voltages (∼10 V DC)─and the adhesion is permanent after the field is turned off. The adhesion is strong enough to allow millimeter-scale capsules/gels to be assembled in 3D into robust structures such as capsule–capsule chains, capsule arrays on a base gel, and a 3D cube of capsules. EA-based assembly of spherical building blocks can be done more precisely, rapidly, and easily than by any alternative techniques. Moreover, the adhesion can be reversed (by switching the polarity of the field)─hence any errors during assembly can be undone and fixed. EA can also be used for selective sorting of charged soft matter─for example, a ‘finger robot’ can selectively ‘pick up’ capsules of the opposite charge by EA and subsequently ‘drop off’ these structures by reversing the polarity. Overall, our work shows how electric fields can be used to connect soft matter without the need for an adhesive or glue.

•The transfer of technology from gasoline to electric vehicles is one of the most effective alternatives for reducing carbon emissions.•Lack of infrastructure and high cost of electric vehicles is the prime hindrance in technology shifting.•Environmental benefits are the primary motivator in selecting electric vehicles.

Particulate matter (PM) is a major air quality concern in Indian cities. Besides health risks, it is associated with climate change impacts. The nature and extent of these impacts depend on the chemical composition of PM. The carbonaceous fraction is especially linked to adverse health impacts and possible global warming. PM10inventory and source emission profiles were used to estimate elemental carbon (EC), organic carbon (OC) and total carbon (TC) emissions in six Indian cities. Thirty source groups were considered. TC emissions in the cities of Bangalore, Chennai, Delhi, Kanpur, Mumbai and Pune are ~20.5, 2.4, 28.8, 3.3, 16.7 and 6.7 TPD, respectively. EC emissions in Bangalore, Delhi, Mumbai and Pune are 7577, 6436, 2737 and 1662kg day−1, respectively. In Chennai (371kg day−1) and Kanpur (600kg day−1), these are comparatively low. Vehicles, road dust and cooking using solid fuel are the key urban sources. Within the vehicle group, heavy duty diesel vehicles are the largest contributors. EC, mostly in the fine size mode, can have larger health implications. Diesel particulates are known to be carcinogenic and also have high EC to OC ratio that signifies their global warming potential. PM emission control from the identified urban sources will offer the twin benefits of reducing health risks and global warming.

Emission inventories can serve as a basis for air quality management programs. The focus has been mainly on building inventories for criteria pollutants including particulate matter (PM). Control efforts in developing countries are mostly limited to total suspended particles (TSP) and/or PM10. Since the adverse effects of PM10depend on its chemical composition, it is important to control emissions of toxic species. The first step is to identify key pollution sources and estimate quantities of various chemical species in emissions. This paper presents a speciated PM10emission inventory for Delhi, the capital and one of the most polluted cities in India. An established PM10inventory for Delhi in conjunction with source profiles was used to estimate emissions of major PM10components including organic and elemental carbon (OC and EC, respectively), sulphates (SO42–), and nitrates (NO3–), as well as selected toxic trace metals (i.e., Pb, Ni, V, As, and Hg), some of which are subject to India’s National Ambient Air Quality Standards (NAAQS). For the base year of 2007, emission estimates for PM10mass, OC, EC, SO42–, and NO3–are 140, 22, 6.4, 2.8, and 2.1 tonnes/day (TPD; 1 tonne = 1000 kg), respectively. Emissions of Pb, Ni, V, As, and Hg are estimated to be 203, 43, 37, 26, and 9.4 kg/day, respectively. This inventory underestimated Pb and Hg emissions because sources of PM10from unorganized secondary lead smelters are not specifically identified and gas-to-particle conversion of Hg is not accounted for.

Gaussian-based dispersion models are widely used to estimate local pollution levels. The accuracy of such models depends on stability classification schemes as well as plume rise equations. A general plume dispersion model (GPDM) for a point source emission, based on Gaussian plume dispersion equation, was developed. The program complex was developed using Java and Visual basic tools. It has the flexibility of using five kinds of stability classification schemes, i.e., Lapse Rate, Pasquill–Gifford (PG), Turner, σ–θ and Richardson number. It also has the option of using two types of plume rise formulations – Briggs and Holland’s. The model, applicable for both rural and urban roughness conditions, uses meteorological and emission data as its input parameters, and calculates concentrations of pollutant at the center of each cell in a predefined grid area with respect to the given source location. Its performance was tested by comparing with 4-h average field data of continuous releases of SO2from Dadri thermal power plant (Uttar Pradesh, India). Results showed that the Turner scheme used with Holland’s equation gives the best outcome having a degree of agreement (d) of 0.522.Gaussian-based dispersion models are widely used to estimate local pollution levels. The accuracy of such models depends on stability classification schemes as well as plume rise equations. A general plume dispersion model (GPDM) for a point source emission, based on Gaussian plume dispersion equation, was developed. The program complex was developed using Java and Visual basic tools. It has the flexibility of using five kinds of stability classification schemes, i.e., Lapse Rate, Pasquill–Gifford (PG), Turner, σ–θ and Richardson number. It also has the option of using two types of plume rise formulations – Briggs and Holland’s. The model, applicable for both rural and urban roughness conditions, uses meteorological and emission data as its input parameters, and calculates concentrations of pollutant at the center of each cell in a predefined grid area with respect to the given source location. Its performance was tested by comparing with 4-h average field data of continuous releases of SO2from Dadri thermal power plant (Uttar Pradesh, India). Results showed that the Turner scheme used with Holland’s equation gives the best outcome having a degree of agreement (d) of 0.522.

Delhi metropolitan area suffers from extreme haze during the post‐monsoon and winter season, impacting climate, public health, and economy. We used a high‐resolution time‐of‐flight aerosol mass spectrometer (HR‐ToF‐AMS) and aethalometer at two urban locations in Delhi to capture non‐refractory PM2.5(NR‐PM2.5) and black carbon (BC) during the post‐monsoon and winter season of 2019–2020. Four haze periods with high composition based‐PM2.5(C‐PM2.5= NR‐PM2.5+ BC) concentration and distinct chemical composition were identified, during all of which organics dominated but with varying contribution (∼[50%–70%] of C‐PM2.5). Biomass burning organic aerosol (BBOA) was dominant in all periods (∼[31%–45%] of OA), but the majority of it was highly aged ∼(45%–50%) with high O/C (0.71 and 0.46 at the two sites), formed most likely through rapid dark oxidation of freshly emitted and partially oxidized BBOA. High polycyclic aromatic hydrocarbons (PAH) signals in the fresh BBOA mass spectra suggest incomplete combustion activities such as open biomass burning emissions as major source. During an agricultural burning event in north‐western India, we estimated that ∼(44%–53%) of total C‐PM2.5(combined contribution of aged BBOA and oxygenated OA) measured in Delhi was influenced by long‐range transported biomass burning emissions. During winter, secondary inorganics constituted a significant fraction apart from organics ∼(48%–55%), mainly in the form of ammonium nitrate (NH4NO3; up to ∼[19%–25%] of C‐PM2.5) and ammonium sulfate (NH4SO4; up to ∼[27%–38%]). Enhanced formation of NH4NO3and related‐secondary organic aerosol (SOA) were linked to nighttime oxidation of BBOA, while NH4SO4and related‐SOA were linked to heterogeneous aqueous phase oxidation under high RH conditions (>90%). During post‐monsoon and winter seasons, Delhi experiences high concentration of fine particles leading to haze events. To design targeted mitigation policies, an understanding of key factors responsible for haze events is needed. We used an advanced suite of instrumentation to capture temporal trends of real‐time particle chemical composition at two urban locations in Delhi. Using advanced receptor modelling and molecular level source signatures, the open burning of biomass material was estimated to be the largest contributor to haze events during post‐monsoon and winter haze events. The post‐monsoon haze pollution was largely affected by the aged biomass burning particles likely from the long‐range transport of agricultural residue burning emissions in the upwind states of Delhi. Winter haze episodes were also highly affected by biomass burning emissions, likely from the local burning of wood, coal and/or roadside trash for heating and/or cooking purposes. However, the winter haze particles contained significant aged inorganic material including ammonium nitrate, ammonium sulfate and ammonium chloride aerosol apart from aged organics. This study also suggests that the nighttime aging of biomass burning emissions contributed in the rapid formation of aged organic and inorganic particles, causing haze events. Four haze periods with distinct PM2.5chemical composition were identified during post‐monsoon and winter season in DelhiBiomass burning organic aerosol was dominant in all haze periods with the majority of it present in highly aged formOpen biomass burning emissions undergo rapid nighttime oxidation resulting in high yields of secondary organic and inorganics Four haze periods with distinct PM2.5chemical composition were identified during post‐monsoon and winter season in Delhi Biomass burning organic aerosol was dominant in all haze periods with the majority of it present in highly aged form Open biomass burning emissions undergo rapid nighttime oxidation resulting in high yields of secondary organic and inorganics