COEVOLUTION OF ORGANIC SUBSTANCES AND SOILS

Fast field cycling NMR relaxometry characterizationof biochars obtained from an industrialthermochemical process

Claudio De Pasquale & Valentina Marsala &

Anne E. Berns & Massimo Valagussa & Alessandro Pozzi &Giuseppe Alonzo & Pellegrino Conte

Received: 28 July 2011 /Accepted: 14 February 2012# Springer-Verlag 2012

AbstractPurpose Biochar has unique properties which make it apowerful tool to increase soil fertility and to contribute tothe decrease of the amount of atmospheric carbon dioxidethrough the mechanisms of C sequestration in soils. Chem-ical and physical biochar characteristics depend upon thetechnique used for its production and the biomass nature.For this reason, biochar characterization is very important inorder to address its use either for agricultural or environ-mental purposes.Materials and methods Three different biochars obtainedfrom an industrial gasification process were selected inorder to establish their chemical and physical peculiaritiesfor a possible use in agronomical practices. They were

obtained by charring residues from the wine-making industry(marc) and from poplar and conifer forests. Routine analysessuch as pH measurements, elemental composition, and ashand metal contents were performed together with the evalua-tion of the cross-polarization magic angle spinning (CPMAS)13C NMR spectra of all the biochar samples. Finally, relax-ometry properties of water-saturated biochars were retrievedin order to obtain information on pore size distribution.Results and discussion All the biochars revealed basic pHvalues due to their large content of alkaline metals. Thequality of CPMAS 13C NMR spectra, which showed thetypical signal pattern for charred systems, was not affectedby the presence of paramagnetic centers. Although para-magnetism was negligible for the acquisition of solid statespectra, it was effective in some of the relaxometry experi-ments. For this reason, no useful information could beretrieved about water dynamics in marc char. Conversely,both relaxograms and nuclear magnetic resonance disper-sion profiles of poplar and conifer chars indicated thatpoplar char is richer in small-sized pores, while larger poresappear to be characteristic for the conifer char.Conclusions This study showed the potential of relaxometryin revealing chemical–physical information on industriallyproduced biochar. This knowledge is of paramount impor-tance to properly direct biochar agronomical uses.

Keywords Biochar . CPMAS 13C NMR . FFC NMR .

Paramagnetic effect . Relaxometry

1 Introduction

“Biochar,” “charcoal,” and “char” are terms used up to now assynonyms. They all indicate charred organic matter obtainedby pyrolysis (or carbonization) of biomass (Lehmann and

Responsible editor: Chris Johnson

C. De Pasquale :V. Marsala :G. Alonzo : P. Conte (*)Dipartimento dei Sistemi Agro-Ambientali,Università degli Studi di Palermo,v.le delle Scienze, edificio 4,90128 Palermo, Italye-mail: pellegrino.conte@unipa.it

A. E. BernsIBG-3: Agrosphere, Institute of Bio- and Geosciences,Forschungszentrum Jülich GmbH,52425 Jülich, Germany

M. ValagussaM.A.C. Minoprio Analisi e Certificazioni S.r.l. c/o FondazioneMinoprio,Viale Raimondi 54,22070 Vertemate con Minoprio, Co, Italy

A. PozziA.G.T. Advanced Gasification Technology S.r.l.,Via Trieste 2,22060 Arosio, Co, Italy

J Soils SedimentsDOI 10.1007/s11368-012-0489-x

Joseph 2009; Brewer et al. 2011; Knicker 2011). However, anattempt to distinguish among them has been recently proposed(Lehmann and Joseph 2009). Both charcoal and biochar areobtained industrially. Nevertheless, while “charcoal” isintended as fuel for heat, filter material, reactant in ironmaking, and colorant in industry and art, the term “biochar”refers to all the charred organic systems which are applieddeliberately to soils in order to achieve two possible aims: (1)improve fertility and (2) contribute to the mitigation of globalclimate changes through carbon sequestration in soils(Lehmann and Joseph 2009; Brewer et al. 2011). “Char” isconsidered as a naturally occurring charred material present inthe environment due to vegetation fires (Lehmann and Joseph2009; Knicker 2011).

From a chemical point of view, all the charred materialsare recognized as polycondensed aromatic systems wherethe degree of polycondensation may differ according to thetechnique used for their production (Warnock et al. 2007;Lehmann and Joseph 2009). As an example, low-temperature-produced chars still contain organic compoundsthat can be used as plant growth agents. However, lowsorption capacity for cations has been observed for thesechars. Conversely, improvement in sorption capacity but adecrease in nutrients has been reported for charred matterproduced at much higher temperatures (Gundale and DeLuca 2006). In addition, the polyaromatic macromolecularstructure of charred biomass is responsible for their chemicaland microbial recalcitrance, which is also the cause for theirlong mean residence time in soils (Knicker 2011).

The physical characteristics of the charred matter dependon the nature of the biomass subjected to the thermal stress.In fact, plant species with many large-diameter cells in thestem tissues can lead towards biochars containing largeramounts of macropores (Lehmann and Joseph 2009). Mac-ropores in biochar applied to soils may enhance soil-draining properties and capacity to retain large moleculessuch as phenolic compounds (Warnock et al. 2007). Basedon this, we can infer that understanding the chemical andphysical properties of biochar is very crucial in order toaddress its agronomical and environmental uses and allowmeaningful pre-application quality assessments.

In the present paper, three biochars obtained from anindustrial gasification process were investigated. In particu-lar, they were produced by using conifer and poplar woodchips from forest maintenance and grape press residues fromthe wine-making industry. All the biochars were subjectedto routine analyses such as pH, elemental and ash contentevaluation, and atomic absorption spectroscopy for themeasurements of the amount of alkaline metals and para-magnetic Fe, Cu, and Mn. Finally, suitability of fast fieldcycling (FFC) NMR relaxometry was evaluated in order toestablish the analytical conditions for the application of thisinnovative technique in biochar chemistry.

2 The industrial thermochemical process for biocharachievement

Thermochemical conversion of biomass, also known asgasification, is usually applied to produce stable fuel—gasand charcoal (Dumbleton 1997). During the process, carbo-naceous feedstock is partially oxidized by heating at temper-atures as high as 1,200°C. The gas, generally described assyngas, is a mixture of carbon monoxide, carbon dioxide,hydrogen, methane, and nitrogen. It is used either to power adiesel-cycle endothermic engine in order to produce bothelectricity and heat or as a ready-to-use fuel.

The steps occurring during gasification in a temperaturegradient can be distinguished as: drying completion, pyrol-ysis, combustion (i.e., oxidation), and gasification (i.e.,reduction). The first step is simply the removal of biomassmoisture. Pyrolysis heats up the carbonaceous particles pro-ducing volatile compounds and char. After pyrolysis, com-bustion of volatile compounds and part of the char occursaccording to the following reactions:

O2+C→CO2 ΔH0−393 kJ mol−1

2H2+O2→2H2O ΔH0−242 kJ mol−1

The energy obtained by the aforementioned reactions isused in the last gasification step. It consists in the reductionof the products of combustion (i.e., CO2, H2O, and somenon-combusted partially cracked pyrolysis products) intocarbon monoxide, hydrogen, and methane through the cat-alytic action of a red-hot charcoal bed. The reactionsinvolved in the gasification step are as follows:

C+CO2→2 CO ΔH0+165 kJ mol−1

C+H2O→CO+H2 ΔH0+123 kJ mol−1

CO+H2O→CO2+H2 ΔH0−42 kJ mol−1

CO2+H2→CO+H2O ΔH0+42 kJ mol−1

C+2 H2→CH4 ΔH0−75 kJ mol−1

Gasifiers are classified according to the way air and/oroxygen is introduced (Basu 2010). In the present study, afixed bed, down draft, open core plant, patented by AGTAdvanced Gasification Technology—Italy (PCT/IB2010/054954 title: Plant and method for the production of gasfrom biomass) was used to produce charcoal from differentfeedstocks. In the AGT gasifier, air passes through a fixedbed of biomass in the down draft direction, and the com-bustible gases are forced out in the same way.

The reduction process generates fine-grained, highlyporous charcoal dust, which is collected in a filter (Basu2010) in order to prevent obstruction of the syngas regular flowin the reactor. Charcoal production yield is usually between 5%and 20% of the parent material (on dry matter basis).

J Soils Sediments

3 FFC NMR relaxometry: theory background

Fast field cycling NMR relaxometry is applied to studymobility of liquid systems in porous media. As a solid porousmedium is suspended in water, water molecules are subjectedto two-dimensional random motions across the solid surface(McDonald et al., 2005). Water stays in the vicinity of thesurface for a short time after which it leaves the surface andreenters the liquid in order to be replaced by another watermolecule. The distribution of water motional frequenciesdepends upon the homogeneity of the surface of the porousmedium. In fact, water confined in small-sized pores is moretightly constrained than that freely moving in larger spaces(Pohlmeier et al. 2009). The distributions of magnetic fields(DMF) generated by the motional fluctuations are responsiblefor the dispersion of the longitudinal (or spin-lattice) relaxa-tion times (T1) occurring when each frequency in DMFmatches the Larmor frequencies (ωL) of the observed nuclei(i.e., 1H).Water near the surface can also interact with surficialparamagnetic ions. The resultant modulation of the localdipolar magnetic field generated by paramagnetism addition-ally contributes to spin-lattice relaxation (Kimmich andAnoardo 2004). The direct relationship between the frequencyof the water motion and the 1H Larmor frequency allowsinvestigation of water dynamics in porous media through themodulation of the intensity of an applied magnetic field. Fastfield cycling NMR relaxometry is based on the fast change ofthe intensity of an applied magnetic field in order to monitorthe variations of 1H T1 values of a dynamic system (Kimmichand Anoardo 2004). Figure 1 shows that the basic FFC NMRexperimental design is based on both a pre-polarized (PP) anda non-polarized (NP) sequence (Kimmich and Anoardo2004). Namely, three steps can be recognized: polarization,relaxation, and acquisition. During the first step of the PPsequence (see Fig. 1a), a longitudinal magnetization is gener-ated through the application of a polarization field (BPOL) for alimited and fixed period of time (polarization time, TPOL).Afterwards, the magnetic field is switched to a new one(relaxation field, BRLX), applied for a period τ during whichthe magnetization intensity relaxes to reach a new equilibriumcondition. Finally, the application of a 1H 90° pulse into anacquisition magnetic field (BACQ) held for a fixed time makesthe magnetization observable and the free induction decay(FID) acquirable. In the NP sequence (see Fig. 1b), BPol isnull. The PP sequence is applied when the relaxation fieldbecomes very low in intensity and enhancement of sensitivityis needed for FID achievement (Ferrante and Sykora 2005).The crossover field between the NP and PP sequences isapproximately retrieved when the relaxation field intensity ishalf of that of the polarization field (Ferrante and Sykora2005).

The longitudinal relaxation time (T1) values of the observednuclei are obtained for every given BRLX through a progressive

variation of the τ values. The relationship between signalintensity and τ can be modeled as:

I tð Þ ¼ I0 exp �t T1=ð Þ½ �k þ y0: ð1Þwhere I(τ) is the 1H signal intensity at each fixed BRLX, I0 is the1H signal intensity at the thermal equilibrium, T1 is the averageproton spin-lattice relaxation time, k is a heterogeneity param-eter related to the stretching of the decay process, and y0 is anoffset parameter. This function, which accounts for the largesample heterogeneity resulting in a multi-exponential behaviorof the decay/recovery curves (Morozova-Roche et al. 1999),can be considered as a superposition of exponential contribu-tions, thereby describing the likely physical picture of somedistribution in T1. Equation 1 has the advantage that it is able tohandle a wide range of behaviors within a single model. Forthis reason, assumptions about the number of exponentials tobe used in modeling nuclear magnetic resonance dispersion(NMRD) data are not necessary.

The NMRD curves are obtained by measuring the longi-tudinal relaxation rates (R101/T1) at each BRLX as described

Fig. 1 Basic scheme for FFC NMR relaxometry acquisition. BPol isthe polarization field applied in the time interval TPol, BRlx is therelaxation field applied at variable τ times, BAcq is the acquisition field,P90 is the 1H 90° pulse applied to retrieve the observable FID, andSWT is the switching time. a Pre-polarized sequence (PP). b non-polarized sequence (NP)

J Soils Sediments

above. NMRD curves reflect the spectrum of re-orientationaland diffusional molecular dynamics. They are described byLorentzian functions with the form reported in Eq. 2 throughwhich information about correlation functions of the micro-scopic fluctuations can be achieved (Albano et al. 1983;Kimmich and Anoardo 2004).

J wLð Þ ¼ tC1þ wLtCð Þ2 ð2Þ

Equation 2 was re-elaborated by Halle et al. (1998) as inEq. 3 in order to account for the stretching of the NMRdispersion profiles as a consequence of the complexity ofthe re-orientational dynamics within the molecular system,the heterogeneous distributions of proton exchange rates,and the heterogeneous distribution of intermolecular dipolecouplings.

J wLð Þ ¼P

citCi

1þ wLtCið Þ2P

cið3Þ

In Eqs. 2 and 3, J(ωL) is the spectral density functiondescribing the distribution of the motion frequencies in amolecular system, ωL is the proton Larmor frequency, ci is afitting parameter, and τCi is the ith correlation time (seebelow). According to the Bloembergen–Purcell–Poundmodel (Luo and Sholl 2002), the proton longitudinal relax-ation rate (R101/T1) is related to the spectral density func-tion by Eq. 4.

R1 ¼ 1

T1¼ a þ b 0:2J wLð Þ þ 0:8J 2wLð Þ½ �: ð4Þ

Here, α represents the high-field relaxation rate, and β isa constant related to the dipolar interactions. The lattercontains the proton quantum-spin number, the proton gyro-magnetic ratio, the Planck constant, and the electron-nuclearhyperfine coupling constant which describes the interactionsbetween resonant protons and unpaired electrons. The com-bination of Eqs. 3 and 4 provides a set of parameters {ci,τCi}which are used to retrieve the average correlation time ( th i)in the form (Halle et al. 1998):

th i ¼P

i citCiPi ci

ð5Þ

Correlation time describes the random molecular motionsof molecular systems either in solution or in porous media(Kimmich and Anoardo 2004). Namely, th i is the time takenfor a molecule to rotate one radian or to move a distance ofthe order of its own dimension (Bakhmutov 2004). Thelonger the th i value, the slower the molecular motions,thereby revealing restrictions in the motional freedomdegrees of spatially restrained molecular systems. Converse-ly, as a molecule encompasses faster motions due to higherdegrees of freedom in larger spaces, shorter correlation time

values are expected. Correlation time values are also affect-ed by paramagnetic impurities. The latter fasten spin-latticerelaxation times, thereby altering the results achievable bycombining Eqs. 3 to 5.

4 Materials and methods

4.1 Biomass samples for biochar production

Biochars were obtained from three different feedstocks: coni-fer wood chips, poplar wood chips, and grape press residues(marc). Conifer wood chips were the result of mountain for-estry management in the northern Italian Apennines (ValleStaffora, 44°45′15″N, 9°13′49″E). The tree species compris-ing the biomass feedstock were: larch (Larix decidua), Scotspine (Pinus sylvestris L.), black pine (Pinus nigra A.), silverfir (Abies alba M.), and spruce (Picea excelsa L.).

Poplar (Populus spp. L.) wood chips were obtained fromdedicated short-rotation forestry in the Po Valley (GadescoPieve Delmona, 45°10′13″N, 10°06′01″E). The age of theforestry at cutting down was 5 years.

Grape press residues were the result of a wine-makingprocess in a winery in the center of Italy (Montefiore dell’Aso,43°03′57″N, 13°46′52″E). The feedstock included stalks,peel, and grape seeds.

4.2 Routine chemical characterization

The amounts of carbon and nitrogen were determined by drycombustion, using an elemental analyzer (NC 2500—CEInstrument, Thermoquest, Milan, Italy). C and N amountswere corrected for the ash content, which was obtained byloss on ignition at a burning temperature of 600°C in anelectric muffle furnace (Table 1).

Biochar pH was determined in water. Prior to pH meas-urements, each sample was treated to retrieve the compactedbulk density (CBD) value, which was needed to calculatesample weight to be taken for water suspensions. CBD wasdetermined in a test cylinder with a nominal capacity of1,000 mL and a diameter of 99–105 mm. Here, each freshsample was subjected to a pressure of 9.17 gcm−2. Aftersuspension in water (specific conductivity ≤0.2 mS m−1 at25°C and a pH≥5.6; char-to-water ratio of 1:5, v/v), pH wasmeasured at 22°C with a standard lab pH meter. pH valuesare reported in Table 1.

4.3 Surface area measurements

Surface area measurements on all the biochars were per-formed via the dynamic Brunauer–Emmett–Teller (BET)method using a Micromeritics Flowsorb 2309 apparatus(Dunstable, UK) with nitrogen as the adsorbate. The biochar

J Soils Sediments

samples were oven-dried at 250°C for 30 min prior to BETanalysis. The surface BET areas of the three biochars studiedhere are reported in Table 1.

4.4 Atomic absorption spectroscopy

Milli-Q water (resistivity of 18.2 MΩ cm at 25°C) usedthroughout the experiments was produced by a Milli-QAdvantage A10 Ultrapure Water Purification System(Millipore Corporation, MA, USA). Trace metal grade con-centrated nitric and perchloric acids were used together with30% hydrogen peroxide for digestion of the metals presentin the biochar samples. A CEM Mars 5 Microwave Accel-erated Reaction System (Bergamo, Italy) was used fordigestion by following the procedure described in Tranchinaet al. (2008). All reactants were purchased from Sigma(Milan, Italy).

A Shimadzu AA-6300 with flame atomization (Milan, Italy)was used for quantification of total recoverable levels of para-magnetic (i.e., Cu, Fe, and Mn) and alkaline (i.e., Ca, K, andNa) metals. The amounts of Cr, Ni, Pb, and Zn were negligibleand are not further considered in this study. All measurementswere done in triplicate. Table 1 reports the concentrations ofNa, K, Ca, Fe, Cu, and Mn of the three biochar samples.

4.5 CPMAS 13C NMR spectroscopy

Cross-polarization magic angle spinning (CPMAS) 13CNMR experiments were carried out on a 7.05-T VarianNMR System (Varian Inc., Palo Alto, CA, USA) with a6-mm Apex HX probe head. The samples were spun at8,500±1 Hz. To ensure homogeneous CP conditions, thesamples were placed in the homogeneous region of the coilas previously described in Berns and Conte (2010). Between50,000 and 100,000 scans were acquired depending on thesample. All FIDs were recorded with a recycle delay of 2 s,an acquisition time of 20 ms, and a sweep width of 25 kHz.Cross polarization was performed with a contact time of5 ms and a 13C r.f. field set at 55 kHz. The 1H r.f. fieldwas centered at the (−1) sideband of the Hartmann–Hahnmatching profile at 46.5 kHz, and an ascending 1H-ramp of17 kHz was applied to ensure homogeneous CP conditions(Berns and Conte 2011). Decoupling was done using aSPINAL sequence with a 1H r.f. field of 54 kHz, a pulsewidth of 11 μs, and a phase of 4.51. The FIDs were acquiredwith VNMRJ version 2.2D software (Varian Inc., Palo Alto,CA, USA) and elaborated with MestReNova 6.1.1 software(Mestrelab Research, Santiago de Compostela, Spain).According to the results reported in Table 1, the Fe/C, Cu/C,andMn/C ratios are «1 for all the samples, thereby making theeffect of the inorganic paramagnetism on CPMAS 13C NMRacquisition negligible (Preston 1996; Smernik and Oades2000a, b; Conte et al. 2001, 2004).T

able

1Phy

sicalandchem

ical

prop

ertiesof

thebiochars

from

theindu

strial

thermochemical

process

Sam

ples

Surface

area

a(m

2g−

1)

pH(inH2O)

Ashes

Cb

Nb

Na

KCa

Fe

Cu

Mn

gkg

−1

Marcchar

42±4

11.1±0.1

360±40

580±40

16±2

0.20

±0.02

9.4±0.4

23.0±0.1

7.5±0.4

0.80

±0.04

0.02

8±0.00

1

Pop

larchar

98±6

9.6±0.1

220±20

680±50

14±1

0.15

±0.01

1.8±0.1

34.0±0.2

0.57

±0.03

0.30

±0.01

0.03

5±0.00

2

Con

ifer

char

66±5

10.3±0.1

80±7

760±70

3.9±0.2

0.18

±0.02

0.87

±0.04

9.8±0.5

1.8±0.1

0.25

±0.02

0.03

2±0.00

1

aBETmetho

dbObtainedon

ash-free

basis

J Soils Sediments

4.6 NMR relaxometry

Dried biochars were prepared as slurry for FFC NMR relax-ometry investigations according to the procedure reported inDunn et al. (2002). For a detailed description of fast fieldcycling NMR relaxometry experiments, the reader is referredto the theory background above and to Kimmich and Anoardo(2004), Anoardo et al. (2001), and Ferrante and Sykora (2005).

1H NMRD profiles (i.e., relaxation rates vs. protonLarmor frequencies, according to Eq. 4) were acquired ona Stelar Smartracer Fast-Field-Cycling Relaxometer (Stelars.r.l., Mede, PV—Italy) at a constant temperature of 25°C.The proton spins were polarized at a polarization fieldcorresponding to a proton Larmor frequency (ωL) of8 MHz for a period of polarization corresponding to aboutfive times the T1 estimated at this frequency. After eachBPOL application, the magnetic field intensity (indicated asBRLX, in Fig. 1) was systematically changed through theproton Larmor frequency ωL range 0.01–8.0 MHz. Theperiod τ, during which BRLX was applied, was varied on16 logarithmic spaced time sets, each of them adjusted atevery relaxation field in order to optimize the sampling ofthe decay/recovery curves. FIDs were recorded following asingle 1H 90° pulse applied at an acquisition fieldcorresponding to the proton Larmor frequency of 7.20 MHz.A time domain of 100 μs sampled with 512 points wasapplied. Field-switching time was 3 ms, while spectrometerdead time was 15 μs. For all experiments, a recycle delay of6 s was used. The non-polarized FFC sequence (see Fig. 1a)was applied when the relaxation magnetic fields were in therange of the proton Larmor frequencies between 8.0 and3.0MHz. A polarized FFC sequence (see Fig. 1b) was appliedin the proton Larmor frequencies BRLX range of 3.0–0.01 MHz (Kimmich and Anoardo 2004).

4.7 FFC NMR data elaboration

R1 values were determined by fitting the 1H magnetizationdecay/recovery curves at each BRLX value (i.e., 1H signalintensity versus τ) to the stretched exponential function (alsoknown as Kohlrausch–Williams–Watts function) reported inEq. 1 after exportation of the experimental data to OriginPro 7.5SR6 (version 7.5885, OriginLab Corporation, Northampton,MA, USA).

Relaxation data at the highest frequency (8 MHz) werealso evaluated by using the UPEN algorithm (Alma MaterStudiorum, Università di Bologna, Italy) (Borgia et al. 1998,2000) with the aim to obtain the T1 distributions at thismagnetic field and therefore information on pore distribu-tions and water interactions. The choice of UPEN analysesonly at 8 MHz was due to the larger NMR sensitivity at thisfrequency as compared to the other proton Larmor frequen-cies (Kimmich and Anoardo 2004).

The NMRD profiles reporting the calculated R1 valuesvs. Larmor angular frequency (ωL) were exported to Origin-Pro 7.5 SR6 and fitted with a Lorentzian function obtainedby combining Eqs. 3 and 4 according to the model-freeanalysis described in Halle et al. (1998) and Luchinat andParigi (2008).

The number n of Lorentzians that was included in Eq. 3without unreasonably increasing the number of parameterswas determined bymeans of the merit function analysis (Halleet al. 1998). For the present study, n03 was used for themathematical fit of the NMRD profiles. The obtained six fitparameters (c1, c2, c3, τ1, τ2, τ3) were used to obtain theaverage correlation time described by Eq. 5. Table 2 reportsthe NMRD parameters achieved by applying Eqs. 3 to 5.

5 Results and discussion

5.1 Biochar inorganic components

Sacier (1983, 1984), Feng et al. (2004), and Cheng et al.(2010) reported that biochar properties are affected by theprocedure applied during production as well as by post-production storage conditions. In particular, Cheng et al.(2010) reported that the partially oxidizing conditions appliedduring the industrial production of biochars can favor forma-tion of surficial oxygenated functional groups. The latter canalso be achieved by biochar exposure to air after the gasifica-tion process (Feng et al. 2004). Finally, Sancier (1983, 1984)stated that the presence of inorganic salts in biomasses mayallow formation of complexes where the metal ions can eitherreplace hydrogens in –CH or –OH groups in the aromaticbiochar structures or electrostatically interact with theπ-orbitals of the extended aromatic structure.

Table 1 shows that the most abundant metals in the samplesstudied here are sodium, potassium, calcium, iron, copper, andmanganese. Their presence in biochar water suspensionscould explain the achievement of pH values above 9. In fact,it is well recognized that Na(I), K(I), and Ca(II) allow analkaline reaction as the porous media, where they are allocated,are suspended in a slurry (White 2005). In particular, as biocharis suspended in water, the following chemical equation issuggested as a possible mechanism for the alkaline reaction

Table 2 Values of relaxometry parameters as obtained by applyingEqs. 3 to 5

Samples α (s−1) β (×106 s−2) <τ>(×10−6 s−1)

Marc char 5.1±0.5 32±3 0.35±0.03

Poplar char 5.4±0.5 7.6±0.8 1.4±0.1

Conifer char 3.2±0.3 8.1±0.8 0.95±0.08

See the text for the meaning of the relaxometry parameters

J Soils Sediments

of the three biochars used in the present study (Mullen et al.2010). It must be pointed out that the possible interactionsbetween biochars and metal ions are those already describedin Sancier (1983, 1984) as reported above.

biochar � ðNaþ;Kþ;Ca2þÞn þ nH2O

! biochar � ðHþÞn þ ðNaþ;Kþ;Ca2þÞn þ nOH� ð4ÞIron, copper, and manganese cations are involved in the

acid reaction reported in the chemical Eq. 5 whereMenþ canbe one of the three transition metals in any of their oxidationstates.

Menþ þ nH2O ! MeðOHÞn þ nHþ ð5ÞThe predominance of the basic over the acid reaction as

supported by the pH values in Table 1 indicates (1) Na+, K+,and Ca2+ can be exchanged with the water protons, beingmainly adsorbed on the biochar surface; (2) iron, copper,and manganese are either conceivably present as oxides orstrictly bound to biochar thus making the contribution of (5)negligible.

5.2 Biochar CPMAS 13C NMR structural features

Figure 2 reports the CPMAS 13C NMR spectra of the threechars used in the present study. All spectra show one broadsignal spanning from 90 to 170 ppm (centered at 126 ppm)and the corresponding spinning sidebands at a distance of113 ppm on each side of the central peak. According toliterature (e.g., Smernik and Oades 2000a, b; McBeath andSmernik 2009; Knicker 2011), the region between 90 and110 ppm is usually assigned to acetal and ketal groups

(present in carbohydrates), the 110–160 ppm region is dueto aromatic systems, and carboxyl groups resonate between160 and 190 ppm. The presence of acetal or ketal C seemedunlikely as no signal was present in the main carbohydrateregion, and the signal in this region was most likely simplyoverlapping aromatic signals. The spectrum of the marc chardisplayed an asymmetric aromatic signal with a small dis-tinctive shoulder between 150 and 160 ppm. Ar–O or Ar–Nsubstituted aromatic carbons usually resonate in a chemicalshift region from 140 to 160 ppm. The possible presence ofa few oxygenated functional groups could be due either topartially oxidative conditions during gasification or to stor-age post-production conditions, which did not preventexposure to air or to both (see above). The very intensearomatic carbon signal in the CPMAS 13C NMR spectra ofall chars has been already reported in literature. It is wellknown that the aromatic condensation in chars increases asthe heat treatment temperature increases (McBeath andSmernik 2009). According to Krull et al. (2009), McBeathand Smernik (2009), and Knicker (2011), the signal at126 ppm is due to the diamagnetic currents produced bydelocalized π-electrons in extended aromatic structures orgraphite-like micro-crystallites. For this reason, conversionof biomass to biochar (i.e., graphite-like structure) at thetemperature of around 1,200°C reached in the gasifierdescribed above can be considered complete.

5.3 1H T1 relaxograms

Table 1 reports the biochar surface areas (SA) as obtained byBETanalyses. The surface areas changed in the order: SAPC>SACC>SAMC. Schure et al. (1985) reported that SA is relatedto pore sizes in porous media. In fact, the smaller the poresizes, the larger is the surface area of the porous medium.Conversely, as the sizes of the pores increase, SA reduction isachieved. According to this, we conclude that pore sizes (PS)in the biochars used for the present study change in the orderPSPC<PSCC<PSMC.

Distributions of longitudinal relaxation times (T1) at afixed proton Larmor frequency (i.e. 8 MHz in the presentstudy) are traditionally related to the porosity of porousmedia (Pohlmeier et al. 2009). T1 is the lifetime of thefirst-order rate process that returns the 1H magnetization tothe Boltzman equilibrium (Bakhmutov 2004). Its magnitudedepends on the nature of the nuclei, the physical state of thesystem, and the temperature. In particular, spin-lattice(or longitudinal) relaxation occurs when the lattice experi-ences magnetic fields fluctuating at frequencies resemblingthose of the observed nuclei (see the theory backgroundabove). Fluctuating fields are generated by molecularmotions which strongly affect dipolar interactions. In thefast motion regime (1 � w2

0t2C), which is the case of the low-

field NMR applied here, the faster the motions are (e.g.,Fig. 2 CPMAS 13C NMR spectra of marc (MC), poplar (PC), andconifer (CC) chars. SSB are the spinning side bands

J Soils Sediments

water in large-sized pores), the lower is the dipolar interactionefficiency, thereby favoring longer T1 values. Conversely,slower molecular dynamics (as for water constrained insmall-sized pores) can be associated with shorter spin-latticerelaxation times due stronger nuclear dipolar interactions(Bakhmutov 2004; Pohlmeier et al. 2009).

Biochar structure and porosity are strongly dependent uponthose of the parent material. In fact, according to the propor-tions of hemicelluloses, cellulose, and lignin in biomass, thethermal processes occurring during charring produce biocharswith different physical properties (Downie et al. 2009). More-over, the amount and nature of the inorganic components(i.e., ashes) have implications for the physical biochar struc-tures. As an example, some processing conditions result in ashfusion or sintering that can induce dramatic changes within thephysical and structural biochar composition (Downie et al.2009).

Figure 3 reports the relaxograms for marc (MC), poplar(PC), and conifer (CC) chars. The relaxograms are broadand complex, spanning several T1 decades with the width ofthe signal (A) decreasing in the order AMC>APC>ACC

(see Fig. 3). This is also the trend for the ash contents:ashMC>ashPC>ashCC (see Table 1). Conversely, carbonand nitrogen change in the order CCC>CPC>CMC andNMC∼NPC>NCC (see Table 1).

Based only on the relaxation mechanism stated above, wemay suggest that the wide signal for the marc char relaxo-gram indicates that the MC pore distribution is wider than inthe other two samples. In particular, two maxima can beidentified in the MC relaxogram of Fig. 3. The first one atthe shortest T1 value (78 ms) can be due to water moleculesdiffusing in the smallest-sized pores, whereas the watertrapped in the largest-sized pores generates the T1 maximum

at 664 ms. On the other hand, both PC and CC samplesrevealed symmetric T1 distributions with the maximum inPC relaxogram centered at 227 ms (see PC relaxogram inFig. 3) and that in CC relaxogram at 369 ms (see CC relaxo-gram in Fig. 3). The symmetry of the PC and CC relaxogramscan be attributed to larger pore size homogeneity as comparedto the MC sample. However, the longitudinal relaxation timesare also influenced by paramagnetism originating either fromunpaired electrons in extended aromatic structures or frominorganic cations. The amount of inorganic paramagneticcenters (i.e., Fe, Cu, and Mn) in the ashes is reportedly largerin MC than in PC and CC (see Table 1). The amount oforganic radicals in the biochars was not determined in thepresent study. The much lower signal/noise ratio in theCP/MAS spectra, however, already hinted that the total con-tent of paramagnetic species was higher in MC than in CC orPC. Regardless of the source of paramagnetism, the T1 distri-bution of MC cannot be compared to those of PC and CC,because of the presumably larger amounts of paramagneticspecies in MC. The T1 distribution of MC is hence not repre-sentative of the pore size distribution as opposed to the relaxo-grams of PC and CC (see Fig. 3). The relaxation rates ofliquids in proton-poor solids (such as biochars) are dependenton the translational diffusion of the liquid in the neighborhoodof the solid surfaces (Korb 2011). Since these surfaces can berich in paramagnetic centers (such as in the case of marc char),the magnetic moments and the local dipolar fields generatedby the unpaired electrons accelerate relaxation rates of theproton spins in the diffusing water. For this reason, alterationof the meaning of the T1 distributions arises. In the case of theMC relaxogram in Fig. 3, the short T1 values cannot be dueonly to water trapped in small-sized pores, but also to theeffect of paramagnetic centers conceivably present in the MCashes. On the other hand, due to the lower amounts of para-magnetic centers, the different positions of PC and CC relaxo-grams (see Fig. 3) can be attributed to a dissimilar pore sizedistribution. Namely, poplar char is richer in small-sizedpores, whereas large pore sizes appear to be characteristicfor the conifer char.

5.4 Qualitative and quantitative aspects of NMRD profiles

Figure 4 reports the NMRD profiles of marc, poplar, andconifer chars. It shows that the longitudinal relaxation ratesvary in the order: R1(MC)>R1(PC)>R1(CC) in the whole rangeof the proton Larmor frequencies (0.01–8 MHz) investigat-ed in the present study.

According to the discussion above, the fastest R1(MC) isdue to the larger amount of paramagnetic metals in marcchar ashes as compared to those found in poplar and coniferchars. On the other hand, the different longitudinal relaxa-tion rates measured for the water-saturated PC and CCsystems can be explained by considering water mobility in

Fig. 3 Spin-lattice relaxation time (T1) distributions of the water-saturated marc (MC), poplar (PC), and conifer (CC) chars obtained atthe proton Larmor frequency of 8 MHz. The ordinate is the percent ofthe total extrapolated signal per Neper (factor of e) of relaxation time(Borgia et al. 1998, 2000)

J Soils Sediments

different-sized pores. In fact, as stated above, pore sizes inpoplar char are, on average, smaller than in conifer char. Forthis reason, the more constrained PC water undergoes fasterrelaxation rates than the freely moving water in CC suspen-sion. The qualitative evaluation of the NMRD profiles inFig. 4 is also confirmed by their quantitative assessmentthrough application of Eqs. 3 to 5. Table 2 reports the valuesof three relaxometry parameters (α, β, and <τ>) for thewater-saturated chars investigated here.

α is the relaxation rate at the high-field plateau of theNMRD profiles (Kimmich and Anoardo 2004). The shorterthe α value, the less constrained are the water molecules tothe porous media resulting in weaker dipolar interactions.On the other hand, when water is tightly bound to biochars,dipolar interactions become stronger, and relaxation ratesbecome faster (Bakhmutov 2004).

β is a measure of the force of the 1H–1H dipolar inter-actions (Halle et al. 1998; Luchinat and Parigi 2008).Increasing dipolar strengths, due to reduced water mobility,produce larger β values. Conversely, weak dipolar couplingsare generated by unbound (or freely moving) water, therebyproviding smaller β values.

The average correlation time <τ> as obtained by Eq. 5 isassociated with the rate of molecular reorientation in thetime unit (Bakhmutov 2004). Namely, slower molecularmotions, due to bound water molecules, produce larger<τ> values, whereas shorter <τ> values are related to fastmolecular movements (see paragraph 3).

Due to the larger amount of paramagnetic centers, theMC values of the three relaxometry parameters reported inTable 2 cannot be compared to those retrieved for the waterdiffusing in poplar and conifer chars. In fact, as protonsinteract with paramagnetic centers, the contribution of theelectron Larmor frequency, the electron g-factor, and the

Bohr magneton in α and β cannot be neglected (Bakhmutov2004). In addition, the correlation times of diffusing water inparamagnetic media are affected by the contribution of theelectron spin relaxation times (Bakhmutov 2004). For thisreason, a comparison between α, β, and <τ> values ofwater-saturated marc and those retrieved for the water dif-fusing in poplar and conifer chars is not possible. Converse-ly, PC and CC relaxometry parameters can be compared toeach other. In particular, Table 2 shows that αPC>αCC

and <τ>PC><τ>CC. These findings are as expected consider-ing PC pores are smaller than CC ones. Due to the large fittingerrors (see Table 2), the β values of both water-saturated PCand CC systems are similar to each other, thereby preventingany possible differentiation between them.

6 Conclusions

The present paper reports a chemical and spectroscopiccharacterization of three chars obtained as by-products ofan industrial gasification process. All the chars revealed abasic pH reaction due to their large ash content rich inalkaline metals. This result, which is in line with otherliterature data for high-temperature-achieved chars, sug-gested that the alkaline metals are mainly adsorbed on charsurfaces. On the other hand, the negligible effect of otherLewis acids, such as the paramagnetic Fe, Cu, and Mn, onthe pH values of the water-saturated chars indicates that thelatter are probably present in the form of stable oxides.

Notwithstanding the different nature of the biomass usedfor biochar production, CPMAS 13C NMR spectroscopywas unable to reveal substantial structural dissimilaritiesamong the chars. Application of fast field cycling NMRrelaxometry on water-saturated chars revealed differencesthat were explained either by accounting for the amount ofparamagnetic centers or for the different pore size distribu-tions. Namely, the char obtained from marc revealed thelargest content of ashes and potentially paramagnetic centers(see Table 1). For this reason, its 1H T1 relaxogram showedthe widest T1 width, and the MC relaxometry parameterswere incomparable with those from PC and CC. Conversely,due to the lower amounts of ashes and paramagnetic centers,the relaxograms of the latter two samples differed because oftheir pore size distributions. In particular, both qualitativeand quantitative evaluation of fast field cycling NMR resultssupported the conclusion that poplar char was made by alarger number of small-sized pores as compared to the charretrieved from conifer residues. These findings were furthersupported by surface BET analyses.

From an analytical point of view, this study shows that caremust be used when interpreting relaxometry data from charredbiomass. In fact, even if the paramagnetic content of eachsample studied here was insufficient to affect acquisition of

Fig. 4 NMRD profiles of marc (MC), poplar (PC), and conifer (CC)chars

J Soils Sediments

high-resolution solid state 13C NMR spectra, FFC NMRrelaxometry was strongly influenced by both ash contentamount and nature. For this reason, a careful evaluation ofthe chemical composition of biochars must be carried out priorto any possible relaxometry data interpretation. Finally, it mustbe stated that this is a preliminary study revealing the suitabil-ity of FFC NMR relaxometry in providing information on thephysical properties of three industrial biochars obtained froma gasification process applied for energy production. It repre-sents the base for the understanding of the potential uses ofsuch industrial by-products in environmental applications.

Acknowledgments P.C. acknowledges Forschungszentrum JülichGmbH (Germany) for having invited him as visiting scientist at theNMRCenter of the Institute of Bio- and Geosciences, IBG-3: Agrosphere.FFC NMR measurements were done at the Università degli Studi diPalermo. The authors are very grateful to Dr. Salvatore Bubici (INVENTOS.r.l.) for the fruitful discussion about FFC NMR relaxometry and toProfessor Heike Knicker (Consejo Superior de Investigaciones Cientifĉas,Spain) for the useful comments on the CPMAS 13C NMR spectra.

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